Journal of Diabetes and Metabolic Disorders最新文献

Aims: This study aimed to establish a decisive threshold for the Glucose Tolerance peak (GTp) parameter in diagnosing Gestational Diabetes Mellitus (GDM) and to assess its diagnostic efficacy in comparison with other commonly employed indexes in clinical practice.

Materials and methods: Conducted as a prospective observational cohort, the study enrolled 92 pregnant women between 24-28 weeks of gestation, who underwent an Oral glucose Tolerance Test (OGTT) 100 gr. following a positive O'Sullivan screening at La Paz University Hospital. An additional 30-min sample was incorporated to assess the insulin response dynamics during hyperglycaemia. Basal indices and those derived from the OGTT 100 gr. test were computed. Receiver Operating Characteristic (ROC) curves were utilized to determine the optimal cut-off points for the indexes derived from the OGTT. Informed written consent was obtained from all participants.

Results: Significantly greater glucose tolerance, as indicated by GTp, was observed in the Non-Gestational Diabetes (NTG) pregnant group (p < 0.01). The GTp emerged as the parameter with the highest positive predictive value for GDM diagnosis. A cut-off of < 0.36 demonstrated 100% specificity and 75% sensitivity in diagnosing GDM.

Conclusions: GTp, an index derived exclusively from the OGTT peak glycaemia, proves valuable in confirming the presence of GDM. The GTp could be used to confirm the presence of GDM under necessity of a second OGTT as test confirmation in pregnant woman. A cut-off of < 0. 36 has a specificity of 100% and a sensitivity of 75% for the diagnosis of GDM.

This case report presents a 9-year-old child without underlying pathology, with a severe life-threatening non-diabetic metabolic ketoacidosis occurring less than 48 h after the onset of fasting and vomiting. The patient was admitted to the pediatric intensive care unit. He received volume expansion and maintenance fluid therapy which allowed a favorable evolution. Because of the unusual rapid onset of intense ketonemia and acidosis, a hereditary metabolic disease was investigated. The association between short fasting period and severe metabolic ketoacidosis has never been described in children outside of the neonatal period. This clinical case emphasizes urgent recognition, rigorous diagnostic and appropriate management in clinical practice.

Objectives: This paper aims to provide a tutorial for diabetologists and endocrinologists on using generative AI to analyze datasets. It is designed to be accessible to those new to generative AI or without programming experience.

Methods: The paper presents three examples using a real diabetes dataset. The examples demonstrate binary classification with the 'Group' variable, cross-validation analysis, and NT-proBNP regression.

Results: The binary classification achieved a prediction accuracy of nearly 0.9. However, the NT-proBNP regression was not successful with this dataset. The calculated R-squared values indicate a poor fit between the predicted model and the raw data.

Conclusions: The unsuccessful NT-proBNP regression may be due to insufficient training data or the need for additional determinants. The dataset may be too small or new metrics may be required to accurately predict NT-proBNP regression values. It is crucial for users to verify the generated codes to ensure that they can achieve their desired objectives.

Purpose: The Discovery of underlying intermediates associated with the development of dyslipidemia results in a better understanding of pathophysiology of dyslipidemia and their modification will be a promising preventive and therapeutic strategy for the management of dyslipidemia.

Methods: The entire dataset was selected from the Surveillance of Risk Factors of Noncommunicable Diseases (NCDs) in 30 provinces of Iran (STEPs 2016 Country report in Iran) that included 1200 subjects and was stratified into four binary classes with normal and abnormal cases based on their levels of triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and non-HDL-C.Plasma concentrations of 20 amino acids and 30 acylcarnitines in each class of dyslipidemia were evaluated using Tandem mass spectrometry. Then, these attributes, along with baseline characteristics data, were used to check whether machine learning (ML) algorithms could classify cases and controls.

Results: Our ML framework accurately predicts TG binary classes. Among the models tested, the SVM model stood out, performing slightly better with an AUC of 0.81 and a standard deviation of test accuracy at 0.04. Consequently, it was chosen as the optimal model for TG classification. Moreover, the findings showed that alanine, phenylalanine, methionine, C3, C14:2, and C16 had great power in differentiating patients with high TG from normal TG controls. Conclusions: The comprehensive output of this work, along with sex-specific attributes, will improve our understanding of the underlying intermediates involved in dyslipidemia.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-024-01384-9.

Background: Metabolic syndrome (MetS) is a cluster of physiological, biochemical, clinical, and metabolic conditions that aggravate the risk of severe diseases such as cardiovascular disease, type 2 diabetes mellitus, and fatty liver. Several dietary molecules have been considered preventive compounds for MetS. Anethole, a natural phenylpropanoid, has been found to protect against MetS and its associated components.

Aim: This systematic review aims to provide an overview of the preclinical evidence supporting the protective effects of dietary anethole against MetS and the associated diseases.

Methods: A literature search was performed using Web of Sciences, PubMed, Scopus, and Google Scholar to identify studies reporting the protective effects of dietary anethole against MetS, without any time restrictions. Review articles, letters to editors, editorials, unpublished results, and non-English papers were excluded from the study.

Results: The results showed that anethole has the potential to effectively protect against the key features of MetS via various mechanisms, including antioxidant and anti-inflammatory effects, stimulating insulin secretion from β-cells, mediating oxidative stress, modulation of the mTOR/PPARγ axis, arterial remodeling, and improvement of vascular relaxation.

Conclusion: Anethole modulates several molecular pathways that are implicated in the pathogenesis of MetS. Future in vitro and animal investigations should be conducted to explore other anti-MetS signaling pathways of anethole. Additionally, well-designed clinical studies are warranted to determine the optimal human dose, bioavailability, and pharmacokinetic characteristics of this dietary compound.

Purpose: The purpose of this study was to evaluate patient, prescriber, and dose characteristics and evaluate changes in glycated hemoglobin (HbA_1c) for patients prescribed once weekly semaglutide for diabetes (OW sema T2D).

Methods: This study was a retrospective claims-based study using the Optum Research Database. The sample included adult patients who had at least one claim for OW sema T2D between Jan 1, 2018, and Dec 31, 2019, were continuously enrolled in the health plan and had a diagnosis of type 2 diabetes (T2DM) during the pre-index or post-index periods. Demographic and clinical characteristics of patients using OW sema T2D were collected, as were the dose and prescriber specialty and the change between pre-index and post-index HbA_1c measures was calculated. Results were stratified by the latest pre-index HbA_1c measurement (HbA_1c greater than or equal to 9.0%, uncontrolled vs. HbA_1c less than 9%, controlled). Statistical comparisons between HbA_1c groups were conducted.

Results: Most patients, 76.3%, were prescribed a 0.25/0.50 mg dose of OW sema T2D. Patients had an overall decrease in HbA_1c of 0.8% and patients with uncontrolled diabetes had a greater reduction in mean HbA_1c compared to those with controlled diabetes (-2.1% vs. -0.3%, p < 0.001). Most patients had their index dose of OW sema T2D prescribed by endocrinologists (27.6%) primary care providers (24.6%) and internal medicine providers (21.6%).

Conclusions: OW sema T2D is an effective real-world T2DM treatment. Future research should further investigate real-world use patterns of this medication.

[This corrects the article DOI: 10.1007/s40200-020-00546-9.].

Purpose: Open-source automated insulin delivery (AID) is used by thousands of people with type 1 diabetes (T1D), but has unknown generalisability to marginalised ethnic groups. This study explored experiences of Indigenous Māori participants in the CREATE trial with use of an open-source AID system to identify enablers/barriers to health equity.

Methods: The CREATE randomised trial compared open-source AID (OpenAPS algorithm on an Android phone with a Bluetooth-connected pump) to sensor-augmented pump therapy. Kaupapa Māori Research methodology was used in this sub-study. Ten semi-structured interviews with Māori participants (5 children, 5 adults) and whānau (extended family) were completed. Interviews were recorded and transcribed, and data were analysed thematically. NVivo was used for descriptive and pattern coding.

Results: Enablers/barriers to equity aligned with four themes: access (to diabetes technologies), training/support, operation (of open-source AID), and outcomes. Participants described a sense of empowerment, and improved quality of life, wellbeing, and glycaemia. Parents felt reassured by the system's ability to control glucose, and children were granted greater independence. Participants were able to use the open-source AID system with ease to suit whānau needs, and technical problems were manageable with healthcare professional support. All participants identified structures in the health system precluding equitable utilisation of diabetes technologies for Māori.

Conclusion: Māori experienced open-source AID positively, and aspired to use this therapy; however, structural and socio-economic barriers to equity were identified. This research proposes strength-based solutions which should be considered in the redesign of diabetes services to improve health outcomes for Māori with T1D.Trial Registration: The CREATE trial, encompassing this qualitative sub-study, was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12620000034932p) on the 20^th January 2020.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-023-01215-3.

Background: SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) has become a global pandemic, and medical experts are scrambling to understand the wide range of symptoms and consequences of the virus. Although acute pancreatitis (AP) and pancreatic damage have been associated with SARS-CoV-2, the mechanism behind this is still unclear. The current article explores whether COVID-19 is an additional cause of AP and diabetic ketoacidosis (DKA). The article illustrates the conditions associated with AP and DKA among COVID-19 patients and diabetes mellitus (DM). Another critical condition is acute kidney injury (AKI), often associated with DKA.

Methods: A search strategy for the article was assigned and retrieved from PubMed, Web of Science, and Scopus databases from 2020 to June 2022. The articles which discussed case studies on AP, DKA, and AKI were included in the study.

Results: The present review of 24 reported case studies represented conditions of AP (12), DKA (5), AP and DKA (5), AP and AKI (1), and DKA and AKI (1) among COVID-19 participants, and showed a potential relationship between the complications.

Conclusion: Healthcare during the COVID-19 pandemic plays a major role among AP, DKA, and AKI-associated COVID-19 patients. A compilation of case studies suggests effective management of COVID-19 infection-related complications such as AP, DKA, and AKI.

Introduction: Physical exercise deters the risk and reduce the adjusted Odds Ratio related to obesity and cardiometabolic diseases but the amount of physical exercise required for initiating those potential advantageous developments in the human body for normal obese individuals is still debatable and thus made many face the health burden during pandemic, despite of their claiming to be physically active.

Objective: The primary aim of this review was to find an ideal duration and form of exercise that could help reduce the risk of cardiometabolic diseases and its complications for subjects with obesity and deranged cardiometabolic risk markers.

Method: Electronic database PubMed/MedLine, Scopus and PEDro for available literature on Experimental studies and RCT on exercise prescription and its effect on anthropometric measurements as well as key biomarkers in obese individuals, 451 records were procured, 47 full text articles were identified to assess eligibility criteria out of which 19 were finally included in the review.

Results: There exists a strong association between cardiometabolic profile and physical activity, poor diet, sedentary lifestyle and continuous exercises for longer duration can lead to reduction in obesity and subjects with cardiometabolic diseases.

Conclusion: Various confounding factors that may affect the outcome of physical activity training have not been considered in a standard format by all the authors in the articles reviewed. There was variation in duration of physical activity and energy expenditure required for inducing the changes in different cardiometabolic biomarkers.