Search Results (28)

Diabetes is a global health challenge, and while current treatments offer relief, they often fall short in achieving optimal control and long-term outcomes. Nanotechnology offers a groundbreaking approach to diabetes management by leveraging materials at the nanoscale to improve drug delivery, glucose monitoring, and therapeutic precision. Early advancements focused on enhancing insulin delivery through smart nanosystems such as tiny capsules that gradually release insulin, helping prevent dangerous drops in blood sugar. Simultaneously, the development of nanosensors has revolutionised glucose monitoring, offering real-time, continuous data that empowers individuals to manage their condition more effectively. Beyond insulin delivery and monitoring, nanotechnology enables targeted drug delivery systems that allow therapeutic agents to reach specific tissues, boosting efficacy while minimising side effects. Tools like microneedles, carbon nanomaterials, and quantum dots have made treatment less invasive and more patient-friendly. The integration of artificial intelligence (AI) with nanotechnology marks a new frontier in personalised care. AI algorithms can analyse individual patient data to adjust insulin doses and predict glucose fluctuations, paving the way for more responsive, customised treatment plans. As these technologies advance, safety remains a key concern. Rigorous research is underway to ensure the biocompatibility and long-term safety of these novel materials. The future of diabetes care lies in the convergence of nanotechnology and AI, offering personalised, data-driven strategies that address the limitations of conventional approaches. This review explores current progress, persistent challenges, and the transformative potential of nanotechnology in reshaping diabetes diagnosis and treatment and improving patient quality of life. Full article

Objectives: This study compared the efficacy of continuous insulin infusion therapy (CIT) versus standard bolus insulin therapy in maintaining optimal perioperative glycemic control in patients with type 2 diabetes mellitus (T2DM) undergoing coronary artery bypass grafting (CABG), focusing on postoperative outcomes. Methods: In this single-center, open comparative study, 214 T2DM patients were selected from 1372 CABG cases (2016–2018) and divided into CIT (n = 28) and bolus therapy (n = 186) groups. Both groups were matched for sex, age, smoking status, body mass index, functional class of angina or heart failure, surgical characteristics and preoperative HbA1c. The target glucose range was 7.8–10 mmol/L (140–180 mg/dL), consistent with current guidelines. Glycemic control was assessed through frequent postoperative measurements, with particular attention to glucose variability and hypoglycemic events. Results: The CIT group demonstrated superior glycemic control, with significantly lower median glucose levels at 7, 8, 10, 12, and 13 h post-CABG (p < 0.05). Glycemic variability was reduced by 32% in the CIT group (p = 0.012), and the incidence of hypoglycemia (<3.9 mmol/L) was 3.6% versus 8.1% in the bolus group. While overall complication rates were similar, the CIT group had 0 cases of stroke, myocardial infarction, or wound infections versus 2.7%, 3.2%, and 5.9%, respectively, in the bolus group. Logistic regression confirmed that each 1 mmol/L increase in first-day glucose levels independently predicted both significant (OR 1.20, 95% CI 1.06–1.36) and serious complications (OR 1.16, 95% CI 1.03–1.30). Conclusions: CIT provided more stable postoperative glycemic control with reduced variability and hypoglycemia risk in T2DM patients after CABG. Although underpowered to detect differences in rare complications, our findings suggest CIT may improve outcomes. These results warrant validation in larger randomized trials. Full article

In the United States, an estimated 38 million individuals (10% of the population) have type 2 diabetes mellitus (T2D), while approximately 97.6 million adults (38%) have prediabetes. Long noncoding RNAs (lncRNAs) are critical regulators of gene expression and metabolism. We were the first to demonstrate that lncRNA Growth Arrest-Specific Transcript 5 (GAS5 (human)/gas5 (mouse)) is decreased in the serum of T2D patients and established GAS5 as a biomarker for T2D diagnosis and onset prediction, now validated by other groups. We further demonstrated that GAS5 depletion impaired glucose uptake, decreased insulin receptor levels, and inhibited insulin signaling in human adipocytes, highlighting its potential as a therapeutic target in T2D. To address this, we developed NPC86, a small-molecule compound that stabilizes GAS5 by disrupting its interaction with UPF-1, an RNA helicase involved in nonsense-mediated decay (NMD) that regulates RNA stability. NPC86 increased GAS5 and insulin receptor (IR) levels, enhanced insulin signaling, and improved glucose uptake in vitro. In this study, we tested the efficacy of NPC86 in vivo in a diet-induced obese diabetic (DIOD) mouse model, and NPC86 treatment elevated gas5 levels, improved glucose tolerance, and enhanced insulin sensitivity, with no observed toxicity or weight changes. A transcriptomics analysis of adipose tissue revealed the upregulation of insulin signaling and metabolic pathways, including oxidative phosphorylation and glycolysis, while inflammatory pathways were downregulated. These findings highlight NPC86’s therapeutic potential in T2D. Full article

Background/Objectives: Among the therapeutic options available for managing PCOS, metformin improves insulin sensitivity, reduces androgen levels, and helps restore menstrual regularity and ovulation. While primarily used for its metabolic effects, metformin therapy may also influence reproductive parameters, including AMH levels, which are pivotal in improving ovarian function and predicting therapeutic outcomes in PCOS. The aim of this study was to search the scientific literature and analyze the correlation between AMH levels and metformin hydrochloride therapy in women with PCOS and IR. Methods: A systematic review of the scientific literature was conducted using the following keywords: polycystic ovarian syndrome, anti-Mullerian hormone, insulin resistance, metformin, treatment, biomarker, and metabolic syndrome. This review was aimed at investigating the potential of AMH as a biomarker of the effectiveness of metformin therapy in patients with PCOS and IR. Results: Metformin treatment in PCOS patients has shown significant reductions in serum AMH levels with prolonged therapy. As an insulin sensitizer, metformin improves insulin sensitivity, reduces hyperinsulinemia, and suppresses hyperandrogenism. This process inhibits the growth of antral follicles, which is reflected in decreased AMH levels. Conclusions: Reductions in AMH levels and improvements in insulin sensitivity can serve as indicators of treatment efficacy and enhancements in reproductive function for these patients. AMH could be considered a prognostic marker for evaluating the effectiveness of metformin therapy. A decrease in AMH levels following treatment may indicate improved ovarian function and a reduction in polycystic morphology. However, further research is necessary to confirm these findings and to determine the optimal dosages and duration of treatment. Full article

Background: Type 1 diabetes (T1D) is a chronic autoimmune disorder characterized by the destruction of pancreatic β-cells, leading to absolute insulin deficiency. Despite advancements in insulin therapy and glucose monitoring, achieving optimal glycemic control remains a challenge. Emerging technologies and novel therapeutic strategies are transforming the landscape of T1D management, offering new opportunities for improved outcomes. Methods: This review synthesizes recent advancements in T1D treatment, focusing on innovations in continuous glucose monitoring (CGM), automated insulin delivery systems, smart insulin formulations, telemedicine, and artificial intelligence (AI). Additionally, we explore biomedical approaches such as stem cell therapy, gene editing, immunotherapy, gut microbiota modulation, nanomedicine-based interventions, and trace element-based therapies. Results: Advances in digital health, including CGM integration with hybrid closed-loop insulin pumps and AI-driven predictive analytics, have significantly improved real-time glucose management. AI and telemedicine have enhanced personalized diabetes care and patient engagement. Furthermore, regenerative medicine strategies, including β-cell replacement, CRISPR-based gene editing, and immunomodulatory therapies, hold potential for disease modification. Probiotics and microbiome-targeted therapies have demonstrated promising effects in maintaining metabolic homeostasis, while nanomedicine-based trace elements provide additional strategies to regulate insulin sensitivity and oxidative stress. Conclusions: The future of T1D management is shifting toward precision medicine and integrated technological solutions. While these advancements present promising therapeutic avenues, challenges such as long-term efficacy, safety, accessibility, and clinical validation must be addressed. A multidisciplinary approach, combining biomedical research, artificial intelligence, and nanotechnology, will be essential to translate these innovations into clinical practice, ultimately improving the quality of life for individuals with T1D. Full article

This review addresses the role of semaglutide (SMG), a GLP-1 receptor agonist, in the treatment of obesity and its related comorbidities. Originally developed for type 2 diabetes (DM2), SMG has shown significant efficacy in weight reduction, with superior results compared to other treatments in the same class. Its effects include appetite suppression, increased satiety, and improvements in cardiovascular, renal, and metabolic parameters. Studies such as SUSTAIN, PIONEER, and STEP highlight its superiority compared to other GLP-1 receptor agonists and anti-obesity drugs. The oral formulation showed promising initial results, with higher doses (50 mg) showing weight losses comparable to those of subcutaneous administration. Despite its benefits, there are challenges, such as weight regain after cessation of treatment, gastrointestinal adverse effects, and variability of response. Future studies should explore strategies to mitigate these effects, identify predictive factors of efficacy, and expand therapeutic indications to other conditions related to obesity and insulin resistance. The constant innovation in this class of drugs reinforces the potential of SMG to transform treatment protocols for chronic weight-related diseases. Full article

Barley sprouts, the germinated and grown leaves of barley, contain various bioactive compounds, including policosanol, saponarin, and lutonarin. The ingestion of barley sprouts may benefit canine weight management, potentially owing to the anti-obesity properties of bioactive compounds. However, there is limited evidence on the efficacy and safety of barley sprout supplementation in dogs. Therefore, through this study, we assessed the impact of barley-sprout-supplemented diet on body weight and health markers in healthy adult beagles over a 16-week period. The results showed a 7.2% reduction in body weight in dogs fed the barley sprout diet. Hematology, complete blood cell count, and blood biochemistry analyses confirmed that all parameters remained within normal ranges, with no significant differences observed between the control and experimental groups. Although the levels of IFN-γ, IL-6, and insulin remained stable, leptin, a hormone associated with body fat, significantly decreased. Further analysis of alterations in the gut microbiota following barley sprout supplementation revealed no significant differences between the control and experimental groups with respect to alpha and beta diversity analysis. The shift at the phylum level, with a decrease in Firmicutes and an increase in Bacteroidetes, resulted in a reduced Firmicutes/Bacteroidetes ratio. Additionally, the abundance of the Ruminococcus gnavus group was high in the experimental group. Functional predictions indicated an enhancement in carbohydrate, amino acid, and cofactor and vitamin metabolism. These findings suggest that a barley sprouts diet is safe for dogs and may offer benefits for weight management through favorable alterations in body weight, hormone levels, and gut microbiota composition. Full article

Anthocyanins compounds, including cyanidin, malvidin, pelargonidin, peonidin, and petunidin, have demonstrated remarkable anti-aging and insulin-sensitizing properties through their interactions with proteins associated with the insulin/insulin-like growth factor signaling (IIS) pathway in Reticulitermes chinensis, employing advanced molecular docking techniques to elucidate strong binding affinities between specific anthocyanins and key proteins such as Pdk1, EIF4E, and Tsc2 in R. chinensis, suggesting a potential mechanism for their anti-aging effects. These findings not only provide critical insights into the therapeutic potential of anthocyanins for mitigating insulin resistance and promoting longevity, but also highlight the efficacy of in silico molecular docking as a predictive tool for small-molecule–protein interactions. Our research opens new avenues for the development of innovative therapeutic strategies targeting age-related diseases. However, further investigations, including a comprehensive chromosomal analysis and in vivo studies, are essential in order to fully elucidate the molecular mechanism underlying these interactions and their physiological implications. The detailed characterization of anthocyanin-binding affinities and their interactions with key regulatory genes presents exciting opportunities for advancement in molecular medicine, pharmacology, and the development of novel nutraceuticals. Full article

Polycystic ovary syndrome (PCOS) is a complex disorder that impacts both the endocrine and metabolic systems, often resulting in infertility, obesity, insulin resistance, and cardiovascular complications. The aim of this study is to investigate the role of intestinal flora and its metabolites, particularly short-chain fatty acids (SCFAs), in the development of PCOS, and to assess the effects of metformin therapy on these components. SCFA levels in fecal and blood samples from women with PCOS ($n=69$) and healthy controls ($n=18$) were analyzed using Gas Chromatography–Mass Spectrometry (GC/MS) for precise measurement. Fecal microbiota were quantitatively detected by real-time polymerase chain reaction (PCR). To assess the efficacy of six months of metformin treatment, changes in the microbiota and SCFAs in the PCOS group ($n=69$) were also evaluated. The results revealed that women with PCOS exhibited a significant reduction in beneficial bacteria (namely, the C. leptum group and Prevotella spp.) alongside a notable overgrowth of opportunistic microorganisms (C. perfringens, C. difficile, Staphylococcus spp., and Streptococcus spp.). An overproduction of acetic acid (AA, $\mathrm{FC}=0.47$, $p<0.05$) and valeric acid (VA, $\mathrm{FC}=0.54$, $p<0.05$) suggests a link between elevated SCFAs and the development of obesity and PCOS. Interestingly, AA in the bloodstream might offer a protective effect against PCOS by ameliorating key symptoms such as high body mass index ($r=-0.33$, $p=0.02$), insulin resistance ($r=-0.39$, $p=0.02$), and chronic inflammation. Although serum SCFA levels showed non-significant changes following metformin treatment ($p>0.05$), the normalization of AA in the gut underscores that metformin exerts a more pronounced effect locally within the gastrointestinal tract. Furthermore, the study identified the most effective model for predicting the success of metformin therapy, based on serum concentrations of butyric acid (BA) and VA, achieving a 91% accuracy rate, 100% sensitivity, and 80% specificity. These promising findings highlight the potential for developing targeted interventions and personalized treatments, ultimately improving clinical outcomes for women with PCOS. Full article

Type 1 diabetes (T1D) mellitus is a chronic illness in children and teens, with rising global incidence rates. It stems from an autoimmune attack on pancreatic β cells, leading to insufficient insulin production. Genetic susceptibility and environmental triggers initiate this process. Early detection is possible by identifying multiple autoantibodies, which aids in predicting future T1D development. A new staging system highlights T1D’s onset with islet autoimmunity rather than symptoms. Family members of T1D patients face a significantly increased risk of T1D. Italy recently passed a law mandating national T1D screening for pediatric populations. Measurements of β cell function continue to be essential in assessing efficacy, and different models have been proposed, but more appropriate biomarkers are mandatory for both progression studies before the onset of diabetes and during therapeutic monitoring. Biomarkers like microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) play key roles in T1D pathogenesis by regulating gene expression. Understanding their roles offers insights into T1D mechanisms and potential therapeutic targets. In this review, we summarized recent progress in the roles of some non-coding RNAs (ncRNAs) in the pathogenesis of T1D, with particular attention to miRNAs, lncRNAs, and circRNAs. Full article

High-Protein Mulberry is a novel strain of mulberry. High-Protein Mulberry leaves (HPM) were the subject of this study, which aimed to investigate its efficacy and underlying mechanisms in modulating glucose and lipid metabolism. A six-week intervention using db/db mice was carried out to assess the effects of HPM on serum lipid levels, liver function, and insulin (INS) levels. qRT-PCR and Western Blotting were employed to measure key RNA and protein expressions in the PI3K/Akt and PPARα/CPT-1 pathways. UHPLC-MS and the Kjeldahl method were utilized to analyze the component content and total protein. Additionally, network pharmacology was employed to predict regulatory mechanism differences between HPM and Traditional Mulberry leaves. The results of the study revealed significant improvements in fasting blood glucose, glucose tolerance, and insulin resistance in mice treated with HPM. HPM notably reduced serum levels of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and INS, while increasing high-density lipoprotein cholesterol (HDL-C) levels. The treatment also effectively mitigated liver fatty lesions, inflammatory infiltration, and islet atrophy. HPM activation of the PI3K/Akt/PPARα/CPT-1 pathway suggested its pivotal role in the regulation of glucose and lipid metabolism. With its rich composition and pharmacodynamic material basis, HPM displayed a greater number of targets associated with glucose and lipid metabolism pathways, underscoring the need for further research into its potential therapeutic applications. Full article

Type 2 diabetes (T2D) is a global public health issue characterized by excess weight, abdominal obesity, dyslipidemia, hyperglycemia, and a progressive increase in insulin resistance. Human population studies of T2D development and its effects on systemic metabolism are confounded by many factors that cannot be controlled, complicating the interpretation of results and the identification of early biomarkers. Aged, sedentary, and overweight/obese non-human primates (NHPs) are one of the best animal models to mimic spontaneous T2D development in humans. We sought to identify and distinguish a set of plasma and/or fecal metabolite biomarkers, that have earlier disease onset predictability, and that could be evaluated for their predictability in subsequent T2D studies in human cohorts. In this study, a single plasma and fecal sample was collected from each animal in a colony of 57 healthy and dysmetabolic NHPs and analyzed for metabolomics and lipidomics. The samples were comprehensively analyzed using untargeted and targeted LC/MS/MS. The changes in each animal’s disease phenotype were monitored using IVGTT, HbA1c, and other clinical metrics, and correlated with their metabolic profile. The plasma and fecal lipids, as well as bile acid profiles, from Healthy, Dysmetabolic (Dys), and Diabetic (Dia) animals were compared. Following univariate and multivariate analyses, including adjustments for weight, age, and sex, several plasma lipid species were identified to be significantly different between these animal groups. Medium and long-chain plasma phosphatidylcholines (PCs) ranked highest at distinguishing Healthy from Dys animals, whereas plasma triglycerides (TG) primarily distinguished Dia from Dys animals. Random Forest (RF) analysis of fecal bile acids showed a reduction in the secondary bile acid glycoconjugate, GCDCA, in diseased animals (AUC 0.76[0.64, 0.89]). Moreover, metagenomics results revealed several bacterial species, belonging to the genera Roseburia, Ruminococcus, Clostridium, and Streptococcus, to be both significantly enriched in non-healthy animals and associated with secondary bile acid levels. In summary, our results highlight the detection of several elevated circulating plasma PCs and microbial species associated with fecal secondary bile acids in NHP dysmetabolic states. The lipids and metabolites we have identified may help researchers to differentiate individual NHPs more precisely between dysmetabolic and overtly diabetic states. This could help assign animals to study groups that are more likely to respond to potential therapies where a difference in efficacy might be anticipated between early vs. advanced disease. Full article

Background: New methods of continuous glucose monitoring (CGM) provide real-time alerts for hypoglycemia, hyperglycemia, and rapid fluctuations of glucose levels, thereby improving glycemic control, which is especially crucial during meals and physical activity. However, complex CGM systems pose challenges for individuals with diabetes and healthcare professionals, particularly when interpreting rapid glucose level changes, dealing with sensor delays (approximately a 10 min difference between interstitial and plasma glucose readings), and addressing potential malfunctions. The development of advanced predictive glucose level classification models becomes imperative for optimizing insulin dosing and managing daily activities. Methods: The aim of this study was to investigate the efficacy of three different predictive models for the glucose level classification: (1) an autoregressive integrated moving average model (ARIMA), (2) logistic regression, and (3) long short-term memory networks (LSTM). The performance of these models was evaluated in predicting hypoglycemia (<70 mg/dL), euglycemia (70–180 mg/dL), and hyperglycemia (>180 mg/dL) classes 15 min and 1 h ahead. More specifically, the confusion matrices were obtained and metrics such as precision, recall, and accuracy were computed for each model at each predictive horizon. Results: As expected, ARIMA underperformed the other models in predicting hyper- and hypoglycemia classes for both the 15 min and 1 h horizons. For the 15 min forecast horizon, the performance of logistic regression was the highest of all the models for all glycemia classes, with recall rates of 96% for hyper, 91% for norm, and 98% for hypoglycemia. For the 1 h forecast horizon, the LSTM model turned out to be the best for hyper- and hypoglycemia classes, achieving recall values of 85% and 87% respectively. Conclusions: Our findings suggest that different models may have varying strengths and weaknesses in predicting glucose level classes, and the choice of model should be carefully considered based on the specific requirements and context of the clinical application. The logistic regression model proved to be more accurate for the next 15 min, particularly in predicting hypoglycemia. However, the LSTM model outperformed logistic regression in predicting glucose level class for the next hour. Future research could explore hybrid models or ensemble approaches that combine the strengths of multiple models to further enhance the accuracy and reliability of glucose predictions. Full article

Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications. One conundrum in developing time-series forecasting models for blood glucose level prediction is to determine an appropriate length for look-back windows. On the one hand, studying short histories foists the risk of information incompletion. On the other hand, analysing long histories might induce information redundancy due to the data shift phenomenon. Additionally, optimal lag lengths are inconsistent across individuals because of the domain shift occurrence. Therefore, in bespoke analysis, either optimal lag values should be found for each individual separately or a globally suboptimal lag value should be used for all. The former approach degenerates the analysis’s congruency and imposes extra perplexity. With the latter, the fine-tunned lag is not necessarily the optimum option for all individuals. To cope with this challenge, this work suggests an interconnected lag fusion framework based on nested meta-learning analysis that improves the accuracy and precision of predictions for personalised blood glucose level forecasting. The proposed framework is leveraged to generate blood glucose prediction models for patients with type 1 diabetes by scrutinising two well-established publicly available Ohio type 1 diabetes datasets. The models developed undergo vigorous evaluation and statistical analysis from mathematical and clinical perspectives. The results achieved underpin the efficacy of the proposed method in blood glucose level time-series prediction analysis. Full article

Diabetes contributes to about 30% morbidity and mortality world-wide and has tidal wave increases in several countries in Asia. Diabetes is a multi-factorial disease compounded by inflammation, dyslipidemia, atherosclerosis, and is sometimes accompanied with gains in body weight. Sphingolipid pathways that interplay in the enhancement of the pathology of this disease may be potential therapeutic targets. Thus, the application of advanced sphingolipidomics may help predict the progression of this disease and therapeutic outcomes in man. Pre-clinical studies using various experimental animal models of diabetes provide valuable information on the role of sphingolipid signaling networks in diabetes and the efficacy of drugs to determine the translatability of innovative discoveries to man. In this review, we discuss three major concepts regarding sphingolipids and diabetes. First, we discuss a possible involvement of a monosialodihexosylceramide (GM3) in insulin–insulin receptor interactions. Second, a potential role for ceramide (Cer) and lactosylceramide (LacCer) in apoptosis and mitochondrial dysfunction is proposed. Third, a larger role of LacCer in antioxidant status and inflammation is discussed. We also discuss how inhibitors of glycosphingolipid synthesis can ameliorate diabetes in experimental animal models. Full article