Current diabetes reviews最新文献

Background: Gestational diabetes mellitus (GDM) affects almost 10%-12% of pregnancies worldwide, threatening maternal and fetal life. Continuous glucose monitoring (CGM) forms the backbone of managing GDM, and the current methodologies largely disregard physiological and behavioral factors, thereby greatly reducing accuracy and clinical interpretability.

Methods: A hybrid deep learning framework was developed by fusing CGM with multi-sensing modality data, including heart rate, activity levels, sleep patterns, and dietary intake. For data preprocessing, Kalman filtering was applied for temporal alignment, adaptive normalization provided outlier handling and imputation, while the CNN-BiLSTM backbone with attention was harnessed for feature extraction. A Multi-Task Attention Fusion Network (MTAFN) was used to predict glucose values and classify GDM risk simultaneously, while SHAP and dynamic smoothing contributed to interpretability sets.

Results: The framework was validated on an extended OhioT1DM dataset with adaptations for pregnancy. It reached a glucose prediction RMSE of 9.8 mg/dL and a GDM risk classification accuracy of 93%. Compared to competitive approaches, the present solution attained a 25% better accuracy on interpretability and an improvement in sensitivity and specificity of about 4-6% across various physiological conditions.

Discussion: The use of multi-sensing data increased prediction robustness by capturing complex physiological dependencies. The SHAP-based interpretability justified the predictions through a physiological lens. With an attention mechanism for feature weighting, it was possible to identify crucial variables like meal intake and nighttime variability in the workflow sets.

Conclusion: The hybrid framework proposed here is reliable for clinically interpretable continuous glucose monitoring and GDM risk predictions. Its application with high reliability can lead to integrating it within clinical protocols for real-time maternal care sets.

Diabetic Foot Ulcer (DFU) is a serious and chronic complication of uncontrolled diabetes, significantly increasing the risk of amputation and mortality in diabetic patients. Conventional wound healing techniques often fail to manage chronic wounds in DFU due to several limitations, which eventually raises the need for advanced, innovative, and efficient treatment strategies for the better management of DFU. Many novel interventions, such as growth factor therapy, stem cell therapy, gene therapy, nanotechnology, bioengineered skin substitutes, 3D-bioprinting, and regenerative medicine, have shown promising roles in wound healing and tissue regeneration, suggesting their potential implications in the treatment and control of DFU. An integrated and multidisciplinary approach was proven to be a promising option for rapid and effective DFU management, eventually reducing the financial burden on the patients and healthcare system. Besides, AI-- driven medical technologies were found to support the healthcare system for the prevention, prediction, diagnosis, and personalized treatment of DFU. The present review article provides a brief overview of DFU and its pathophysiology. It highlights traditional DFU treatment strategies, their limitations, and focuses on emerging innovative strategies, emphasizing their potential for addressing DFU. Moreover, it sheds light on challenges and future research areas of these emerging interventions.

This work overviews some complex molecular interactions and phosphorylation events in the insulin receptor (IR) signaling pathway and explains its central role in metabolic control, which starts from the synthesis and secretion of insulin by pancreatic β-cells under elevated blood glucose. The triggered pathway coordinates a cascade of molecular processes that results in the activation of primary metabolic functions. Insulin bound to its receptor starts a sequence of events, such as the autophosphorylation of the receptor β subunit for initiating downstream signaling cascades, glycogen synthesis, and the appropriate regulation of lipid metabolism. The complexity and specificity of the signaling pathway involve insulin receptor substrates, phosphatidylinositol-3-kinase (PI3K), and protein kinase B (PKB). A detailed molecular interaction analysis in the IR has pointed to the crucial role of some residues and structural elements necessary for enzymatic functionalities and substrate binding. These factors include the kinase domain of the IR with specific amino acid residues, and subsequent activation of downstream signaling proteins. The structural changes on phosphorylation promote the binding of SH2 domain-containing adaptor proteins, which lead to the initiation of multifunctional signaling complexes central to insulin signal transduction. These molecular mechanisms provide insight into pathophysiology relating to metabolic diseases and potential treatment targets. This review aims an understanding of insulin receptor operation, elucidating the molecular intricacies behind the eventful metabolic insulin signaling pathways and highlighting possible research and therapeutic development in the field of medicine.

Introduction: Diabetes and sarcopenia are major age-related health issues in the world. Due to their bidirectional relationship, they significantly impact the quality of life of elderly men and women, especially those over 60 years old. In this brief communication, we incorporate the effect of sarcopenia into a previously developed mathematical model examining the impact of exercise on the dynamics of glucose and insulin.

Methods: Extending a previous mathematical model by Derouich and Boutayeb, derived from the minimal model of Bergman, we present a new mathematical model using ordinary differential equations in which the parameter "S" is included to express the additive effect of sarcopenia.

Results: Considering that the sarcopenia effect is introduced multiplicatively, the new model proposed shows that sarcopenia lowers insulin sensitivity both in the presence and absence of physical exercise. The effects of sarcopenia on glucose dynamics are illustrated by simulations with varying model parameters.

Discussion: A large number of clinical studies have shown the link between Type 2 diabetes and sarcopenia. Some authors have particularly explored the causal relationship between insulin resistance and sarcopenia. The proposed mathematical model indicates that sarcopenia reduces insulin sensitivity in both the presence and absence of physical exercise, and that severe sarcopenia may negate the benefits of physical exercise.

Conclusion: By theoretically demonstrating the relationship between sarcopenia and Type 2 diabetes, this mathematical model confirms the interrelationship between sarcopenia and Type 2 diabetes for any population worldwide, regardless of its regional, cultural, or environmental specificity.

Bullous pemphigoid (BP) is an autoimmune blistering disease that especially presents in elderly patients, but recently, its incidence has increased in patients treated with inhibitors of dipeptidyl peptidase-4 (DPP-4), among which teneligliptin is widely prescribed as an antidiabetic drug for the treatment of type 2 diabetes mellitus (T2DM). Recent studies describe the association of teneligliptin with the onset of BP, hypothesizing that DPP-4 inhibition could induce immune dysregulation able to trigger autoimmune responses against skin antigens BP180 and BP230. This mini-review discusses the immunomodulatory effects of teneligliptin, immune responses, and chronic inflammation associated with diabetes that predispose patients to BP. It also discusses the clinical implications, such as monitoring for BP in T2DM patients on teneligliptin therapy. Elucidation of this relationship may help in optimizing management strategies by highlighting discontinuation or alternative therapies in the case of a high risk of development of BP.

Introduction: Globally, type 2 diabetes mellitus (T2DM) is a common metabolic disorder affecting millions of people annually. Metformin, a prevalent prescribed antihyperglycemic medication for T2DM, has been associated with vitamin B12 deficiency. This study aimed to determine the prevalence of vitamin B12 deficiency in T2DM patients receiving metformin.

Methods: This retrospective record review study was based on T2DM patients receiving metformin at a single tertiary healthcare center. The prevalence of vitamin B12 deficiency is defined as a serum level <145 pmol/L. Statistical analysis assessed the association between vitamin B12 deficiency and metformin usage.

Results: A total of 246 T2DM patients were included in the study. Among patients who used metformin, 7.7% were found to have vitamin B12 deficiency compared to 4.8% among those who did not use metformin, while the difference was not statistically significant (p=0.571). However, the median (IQR) level of vitamin B12 among metformin users (296 (186)) was significantly lower than that among patients who do not use metformin (382 (370)), p=0.001.

Discussion: The study's findings shed light on the important role of routine screening of vitamin B12 levels in patients on metformin, especially those on long-term use, for early identification and management of deficiency, leading to improved patient outcomes.

Conclusion: Our study revealed a significantly lower level of vitamin B12 among metformin users in T2DM patients. However, the prevalence of vitamin B12 deficiency among metformin users did not differ significantly from those who do not use metformin.

Introduction: Type 2 Diabetes Mellitus (T2DM) is marked by insulin resistance and chronic hyperglycemia. Yoga, a complementary therapy, may improve metabolic outcomes when used with standard care. This systematic review and meta-analysis aimed to evaluate the effects of yoga on glycemic control, insulin resistance, oxidative stress, and psychological well-being in individuals with or at risk of T2DM.

Methods: We conducted a systematic search in PubMed, CAM-QUEST®, and the Cochrane Central Register following PRISMA guidelines. We included randomized controlled trials (RCTs) assessing yoga interventions (asanas, pranayama, kriyas, and meditation) alongside standard care. Primary outcomes were fasting blood glucose (FBS), postprandial blood glucose (PPBS), HbA1c, serum insulin, and HOMA-IR. Secondary outcomes included oxidative stress markers (glutathione, malondialdehyde [MDA], superoxide dismutase [SOD]) and psychological outcomes. Meta-analyses were conducted using random-effects models in RevMan 5.4.1.

Results: Fourteen RCTs (n = 1,629 participants) were included. Yoga combined with standard care significantly improved FBS (SMD = -1.60; 95% CI: -2.27 to -0.92), PPBS (SMD = -2.16; p = 0.03), HbA1c (SMD = -0.92; 95% CI: -1.59 to -0.26), and HOMA-IR (SMD = -1.28; p = 0.002). MDA levels were significantly reduced, though glutathione and SOD showed no significant changes. Psychological well-being improved in several trials.

Discussion: Yoga appears effective in improving glycemic outcomes and insulin resistance in patients with or at risk of T2DM. It may also reduce oxidative stress and enhance psychological well- being, highlighting its potential as a holistic intervention.

Conclusion: Yoga, when integrated with standard care, offers clinically relevant benefits in managing T2DM and related metabolic risks. Further high-quality, multi-centered trials using standardized protocols are needed to validate and generalize these findings.

Diabetic Neuropathy (DN) is the major chronic complication in diabetic patients. Exact pathophysiological mechanisms of DN are not explored well, although axon Schwann cell and microvascular endothelial communication network failure play a major contributing role in DN. The multiple pathophysiological mechanisms of DN are regulated by microRNAs (miRNAs), including inflammation, vascularization, angiogenesis, posttranscriptional regulation, intercellular communication, and signalling pathways. Various types of miRNA affect the gene expressions within cells, but their profiles often change during DN, including SMAD, PI3K, NF-KB, and MAPK. DN has been associated with the miRNAs-9, miRNA-106, miRNA-182, miRNA-23a, miRNA- 23b, miRNA-23c, miRNA-503, miRNA-203, miRNA-145, and miRNA-126. MiRNA dysregulation is one of the first molecular changes seen in diabetics. Therefore, miRNAs have potential as therapeutic targets and biomarkers. This study aims to discuss the importance of miRNA in clinical pathophysiology, diagnosis, signalling pathways, and therapeutic targets for DN.

Insulin resistance is an endocrine disorder associated with various diseases, including diabetes, metabolic syndrome, cardiovascular diseases, and cancer. However, severe insulin resistance differs significantly from common insulin resistance regarding the causes and treatment approaches. Severe insulin resistance is a group of rare disorders characterized by hyperglycemia and hyperinsulinemia, with or without hypoglycemia, along with various metabolic abnormalities and diverse clinical manifestations. Owing to their rarity and complexity, these conditions can be easily misdiagnosed as common diabetes and are often overlooked. Severe insulin resistance is typically reported in individual case studies, lacking comprehensive summaries. This article provides a detailed overview of the different types of severe insulin resistance based on the specific sites of insulin signaling defects. It includes pre-receptor signaling defects, such as insulin autoimmune syndrome, which results from insulin autoantibodies; receptor-level insulin resistance syndromes, including type A and type B insulin resistance syndromes; and post-receptor signaling defects, such as lipodystrophy syndrome. We describe the causes, clinical symptoms, diagnosis, and treatment of extreme insulin resistance and differentiate between these diseases. In this review, we aim to assist physicians in identifying the causes of severe insulin resistance early and in providing individualized treatment for patients, ultimately improving clinical outcomes.

Introduction: Clinical trials indicate that IDegLira is effective in treating type 2 diabetes mellitus (T2DM). This study aims to assess the efficacy and safety of IDegLira in type 2 diabetes mellitus (T2DM) comprehensively.

Methods: To identify relevant randomized controlled trials, we searched PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov. Risk Ratios (RR) and 95% Confidence Intervals (CI) were calculated using the Mantel-Haenszel approach for dichotomous outcomes. Mean Difference (MD) and 95% CI calculated by the inverse variance approach were applied to continuous outcomes.

Results: Twelve randomized controlled trials involving 7628 participants were included in this study. Compared with control groups, IDegLira has a significant hypoglycemic effect in reducing hemoglobin A1c (MD = -0.66; 95% CI [-0.85, -0.47]; p < 0.00001), fasting plasma glucose (MD = -0.90; 95% CI [-1.40, -0.41]; p = 0.0003), self-measured plasma glucose (MD = -0.82; 95% CI [-1.22, -0.42]; p < 0.0001) and achieving the hemoglobin A1c level of < 7.0%(RR = 1.66; 95% CI [1.44, 1.92]; p < 0.00001) or < 6.5% (RR = 2.13; 95% CI [1.76, 2.57]; p < 0.00001). IDegLira outperforms insulin in achieving the target of HbA1c < 7.0 or < 6.5% without hypoglycemia and weight gain. Besides, IDegLira did not increase the incidence of adverse events and serious adverse events.

Discussion: In this section, IDegLira's benefits on simultaneously achieving glycemic control, weight loss, and reduced hypoglycemia risk were summarized. The statistical results were carefully interpreted in conjunction with clinical concerns regarding T2DM complications, adverse effects, and cost-effectiveness differences. An expanded discussion was conducted on integrating individualized HbA1c goals as a dual endpoint, without increasing body weight or the risk of hypoglycemia. Finally, the limitations of the present study are indicated.

Conclusions: IDegLira exhibits a favorable glycemic control effect and acceptable adverse effects in Type 2 Diabetes Mellitus (T2DM). Superior performance in the target glycemic control, particularly suitable for T2DM patients who do not reach the target hemoglobin A1c and have comorbid CVD or obesity.