Clinical Medicine Insights-Endocrinology and Diabetes最新文献

Background: Diabetic kidney disease (DKD) is the main cause of end-stage renal disease and has a high mortality rate. Currently, no effective treatments are available to reduce the progression of kidney damage associated with diabetes.

Objectives: To explore the influence and predictive value of the atherogenic index of plasma (AIP) on early chronic kidney disease and liver injury in patients with type 2 diabetes mellitus (T2DM).

Methods: Medical records of 1057 hospitalized adult patients with T2DM between January 2021 and December 2022 were collected. The predictive value of AIP, renal function, and liver injury in patients with T2DM were analyzed using Pearson's correlation, multiple logistic regression, and receiver operating characteristic (ROC) curve analyses.

Results: AIP was a sensitive indicator of early liver and kidney injury in patients with T2DM. Patients in the DKD group showed increased AIP that positively correlated with serum creatinine, uric acid, and β2-microglobulin levels. Increased AIP negatively correlated with estimated glomerular filtration rate (eGFR). AIP significantly correlated with alanine aminotransferase and aspartate aminotransferase levels and glutamyl transpeptidase-to-platelet ratio (GPR). An eGFR of 60-100 mL/min/1.73 m² significantly increased the risk of DKD as the AIP increased. At lower GPR levels, the risk of DKD significantly increased with increasing AIP. However, no significant difference was found between the 2 groups when the GPR was >0.1407. The ROC curve analysis showed that AIP could predict early liver injury.

Conclusions: AIP is directly involved in early liver and kidney injury in T2DM and may be a sensitive indicator for early detection.

We describe 2 families with 5 members from 2 generations whose clinical and laboratory characteristics over up to 15 years were consistent with dysglycemia/impaired glucose tolerance. In both families (2 probands and 3 family members), long-term follow-up excluded diabetes type 1 and type 2. Diabetes type 1 antibodies were persistently negative and C-peptide levels were normal. In Family 1, the proband, during a follow-up of 7 years (10.3-17.5 years of age), exhibited persistently high HbA1c (>5.7%) with fasting blood glucose levels mostly higher than 100 mg/dl and postprandial glucose levels up to 180 mg/dl. She eventually required oral anti-diabetics with an improvement in glycemic balance. The father and sister also had persistent mild hyperglycemia with borderline high HbA1c (mostly > 5.7%) levels over 15 and 6.2 years respectively. In Family 2, the proband exhibited borderline high fasting hyperglycemia (>100 mg/dl) at age 16.2 years with increasing HbA1c levels (from 5.6%-5.9%) and impaired glucose tolerance at age 18.3 years (2 h blood glucose 156 mg/dl after 75 g glucose). His sister also exhibited borderline hyperglycemia with borderline high HbA1c over 2 years (13.6-15.4 years). These subjects shared a unique phenotype. They are tall and slim with decreased BMI. Three subjects from Generation II failed to thrive during infancy. In view of the data from 2 generations suggesting maturity-onset diabetes of the young (MODY) with autosomal dominant inheritance, we sought to analyze the MODY genes. In Family 1, the molecular analysis by the MODY panel including 11 genes and whole exome sequencing did not detect any mutation in the proband. In Family 2, the MODY panel was also negative in the proband's sister. These families may represent a hitherto unidentified syndrome. Unique features described in this report may help to reveal additional families with similar characteristics and to decipher the molecular basis of this syndrome. In selected cases, oral antidiabetics in adolescents may improve the glycemic balance.

Objective: There is no study on the relationship between triglyceride-glucose index (TyG index) and skeletal muscle mass in middle-aged and elderly C population. Therefore, the aim of the study is to investigate the relationship between the TyG index and weight-adjusted relative skeletal muscle index (RSMI) in middle-aged and elderly C population.

Methods: We retrospectively studied 947 aged ⩾40 years subjects who got a routine medical examination in the Department of Geriatrics of R Hospital from May 2021 to March 2023. The RSMI was designed to evaluate skeletal muscle mass and calculated based on lean mass of the limbs(kg)/body weight(kg) × 100%. Skeletal muscle mass reduction was defined as a RSMI of 1-2 standard deviations (SD) below of healthy adults aged 30-49 years old. Considering the quartile groups of the TyG index, the subjects were assigned to 4 groups: Q1 (less than or equal to 8.171), Q2 (from 8.172 to 8.569), Q3 (from 8.570 to 8.992), and Q4 (greater than or equal to 8.993).

Results: With TyG index increased, RSMI levels significantly reduced(P < .001). Spearman's correlation analysis showed that the TyG index was negatively correlated with RSMI in males (r = -0.320) and females (r = -0.240). The TyG index was positively correlated with body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), fasting plasma glucose (FPG), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) (P < .05). Besides, binary logistic regression analysis showed that the risk of developing reduced skeletal muscle mass in the group Q4 was 2.131 (95%CI:1.118-4.064) in males; and was 2.472 (95%CI:1.581-3.867) in females compared to the Q1 group.

Conclusion: TyG index was negatively correlated with relative skeletal muscle index, and a higher TyG index was associated with the development of reduced skeletal muscle mass independently of other influencing factors. Therefore, the TyG index promises to be a predictor of skeletal muscle mass loss.

Objectives: Diabetes in the 21st century presents one of the greatest burdens of disease on the global population. Digitally mediated interventions have become imperative in alleviating this disease epidemic. We aimed to systematically review randomized controlled trials (RCTs) on different health technologies for preventing Type 2 diabetes mellitus, and their efficacy in decreasing diabetes risk-related outcomes in at-risk patients in comparison to standard care.

Methods: Five electronic databases were searched between October 2021 and December 2022. Studies including digital health technology interventions used for preventing diabetes development by reducing diabetes risk-related outcomes in at-risk adults (⩾18 years) were identified. Data on glycemic levels, incidence of T2DM, weight, and intervention descriptions were extracted, and the risk of bias (ROB) was assessed.

Results: Nine studies met the inclusion criteria and 5 studies (56%) achieved clinically significant outcomes in at least one of the following: decreased weight (22%), glycemic levels (22%), or incidence of T2DM (11%). Two of the 3 (67%) computer-based interventions effectively reduced the HbA1c levels and mean weight of their study population, and 3 of 6 (50%) mobile based interventions (text messages, mobile app, and telehealth) decreased the incidence of T2DM and HbA1c levels. Four studies each had an overall low ROB and one had a high ROB due to attrition.

Conclusion: Preliminary evidence identified in our review demonstrated that health technologies for diabetes prevention are effective for improving diabetes risk-related outcomes. Future research into digital technology protocol and studies of longer duration and more diverse populations are needed for clinical feasibility.

Introduction: An essential process affecting the course of type 1 diabetes (DM1) is the appearance and duration of clinical remission. One of the most important factors promoting the occurrence of remission is physical activity, due to increased activity of antioxidants, reduces insulin resistance and improves glucose transport. Maximal oxygen capacity (VO₂max) is an objective measure of the body's aerobic capacity. To assess VO₂max, oxygen uptake should be measured directly during the exercise test. The aim of the study was to evaluate the physical capacity in adults with DM1 and its relationship with the occurrence of partial clinical remission (pCR) during 2 years follow-up.

Methods: The pCR was assessed by the following mathematical formula: A1c (%) + [4 × insulin dose (U/kg/d)]. The result ⩽9 indicates pCR. VO₂max was assessed between 6th and 24th month of diabetes duration using an ergospirometer (COSMED K5 System), during an exercise test carried out on a cycloergometer (RAMP incremental exercise test).

Results: The study group consisted of 32 adults with DM1. People with pCR were proved to have higher VO₂max level [36.0 (33.0-41.5) vs 30.9 (26.5-34.4) ml/min/kg, P = .009. Univariate and multivariate regression confirmed a significant association between VO₂max and presence of pCR [AOR 1.26 (1.05-1.52), P = .015]. Duration of remission was longer among group with higher VO₂max results [15 (9-24) vs 9 (0-12) months, P = .043]. The positive relationship was observed between diabetes duration and VO₂max (rs = 0.484, P = .005). Multivariate linear regression confirms a significant association between remission duration and VO₂max (ml/min/kg) (β = 0.595, P = .002).

Conclusion: The higher VO₂max, the better chance of partial clinical remission at 2 years of DM1 and longer duration of remission.

Background: The use of ChatGPT and artificial intelligence (AI) in the management of metabolic and endocrine disorders presents both significant opportunities and notable risks.

Objectives: To investigate the benefits and risks associated with the application of ChatGPT in managing diabetes and metabolic illnesses by exploring the perspectives of endocrinologists and diabetologists.

Methods and materials: The study employed a qualitative research approach. A semi-structured in-depth interview guide was developed. A convenience sample of 25 endocrinologists and diabetologists was enrolled and interviewed. All interviews were audiotaped and verbatim transcribed; then, thematic analysis was used to determine the themes in the data.

Results: The findings of the thematic analysis resulted in 19 codes and 9 major themes regarding the benefits of implementing AI and ChatGPT in managing diabetes and metabolic illnesses. Moreover, the extracted risks of implementing AI and ChatGPT in managing diabetes and metabolic illnesses were categorized into 7 themes and 14 codes. The benefits of heightened diagnostic precision, tailored treatment, and efficient resource utilization have potential to improve patient results. Concurrently, the identification of potential challenges, such as data security concerns and the need for AI that can be explained, enables stakeholders to proactively tackle these issues.

Conclusions: Regulatory frameworks must evolve to keep pace with the rapid adoption of AI in healthcare. Sustained attention to ethical considerations, including obtaining patient consent, safeguarding data privacy, ensuring accountability, and promoting fairness, remains critical. Despite its potential impact on the human aspect of healthcare, AI will remain an integral component of patient-centered care. Striking a balance between AI-assisted decision-making and human expertise is essential to uphold trust and provide comprehensive patient care.

This review evaluates the current evidence on the safety and efficacy of sodium-glucose cotransporter 2 (SGLT2) inhibitors for patients with type 2 diabetes mellitus (T2DM) fasting during Ramadan. All studies included in the review were conducted in Asia and the Middle East. Overall, the evidence suggests that SGLT2 inhibitors are a safe and effective treatment option for most T2DM patients fasting during Ramadan. The average incidence of symptomatic hypoglycemia is 12.5%, but ranges from 0.7% to 27%, depending on the study population and concomitant use of other medications. The risk of hypoglycemia is increased when SGLT2 inhibitors are used in combination with insulin and/or sulfonylureas. Therefore, patients taking SGLT2 inhibitors in combination with insulin and/or sulfonylureas can take steps to mitigate this risk, such as having their insulin and/or sulfonylurea doses adjusted and being closely monitored for hypoglycemia. Patients taking SGLT2 inhibitors may be at increased risk of dehydration. To mitigate the risk of dehydration, patients should be advised to consume adequate fluids during the fast-breaking hours. Further research is warranted to validate these findings and extend their applicability to high-risk populations and other regions of the world.

Obesity and diabetes mellitus are prevalent metabolic disorders that have a detrimental impact on overall health. In this regard, there is now a clear link between these metabolic disorders and compromised bone health. Interestingly, both obesity and diabetes lead to elevated risk of bone fracture which is independent of effects on bone mineral density (BMD). In this regard, gastrointestinal (GIT)-derived peptide hormones and their related long-acting analogues, some of which are already clinically approved for diabetes and/or obesity, also seem to possess positive effects on bone remodelling and microarchitecture to reduce bone fracture risk. Specifically, the incretin peptides, glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), as well as glucagon-like peptide-2 (GLP-2), exert key direct and/or indirect benefits on bone metabolism. This review aims to provide an initial appraisal of the relationship between obesity, diabetes and bone, with a focus on the positive impact of these GIT-derived peptide hormones for bone health in obesity/diabetes. Brief discussion of related peptides such as parathyroid hormone, leptin, calcitonin and growth hormone is also included. Taken together, drugs engineered to promote GIP, GLP-1 and GLP-2 receptor signalling may have potential to offer therapeutic promise for improving bone health in obesity and diabetes.