Glycolipid metabolism and microRNAs (miRNAs) play important roles in hepatitis C virus (HCV)-induced type 2 diabetes mellitus (T2DM). Our study aimed to explore the mechanism of action of miR-378b in liver glycolipid metabolism disorders caused by HCV infection. An in vitro model was established by HCV infection of HepG2 cells, and an animal model was constructed by HCV infection of hCD81/hOccludin double-transgenic mice. The levels of related genes, proteins and glycolipid metabolism products were detected by RT‒qPCR, Western blotting, pathological tissue staining and reagent kits. Our findings revealed increased expression of miR-378b in T2DM patients, mice, and liver cells infected with HCV. Reducing miR-378b levels in liver cells and mice infected with HCV can increase glycogen production and decrease lipid accumulation and TG concentrations. Additionally, reductions in TG, TC, LDL, and FFA levels, along with increased HDL levels, were detected in the serum of mice infected with HCV with miR-378b knockdown. The findings revealed that HCV triggers glycolipid metabolism dysregulation in the liver through increased miR-378b expression. From a mechanistic standpoint, HCV inhibits the activation of the E2F2/PI3K/AKT signaling pathway by upregulating miR-378b expression, thereby causing disorders of glycolipid metabolism in the liver. The results of this study provide theoretical support for the use of miR-378b as a new target for the treatment of HCV-induced liver glycolipid metabolism dysregulation.

BACKGROUND:Sepsis-induced organ dysfunction poses a significant clinical challenge with limited therapeutic options. This study investigated the therapeutic potential of the glucagon-like peptide-1 receptor agonist (GLP-1RA) liraglutide in sepsis and its underlying mechanisms, focusing on modulation of the gut microbiota-derived metabolome. METHODS:Public transcriptomic data analysis identified overlapping targets between liraglutide and sepsis-related genes. In a murine cecal ligation and puncture (CLP) model, liraglutide treatment was evaluated for its effects on survival, systemic inflammation, and organ injury. The gut microbiota composition and fecal metabolome were assessed via 16S rRNA sequencing and UPLC-MS. We also measured plasma GLP-1 in sepsis patients and examined the microbiota-dependency of liraglutide's effects using antibiotic-depleted mice and fecal microbiota transplantation (FMT) from liraglutide-treated mice. Additionally, citrulline, a key identified metabolite, was functionally validated both in vitro and in a clinical cohort. RESULTS:Liraglutide significantly improved survival, reduced pro-inflammatory cytokines, and alleviated lung, liver, and colon damage in septic mice. It partially restored sepsis-induced gut dysbiosis and modulating associated metabolites, including increasing citrulline. The survival benefit of liraglutide was abolished in microbiota-depleted mice, while FMT from liraglutide-treated mice conferred protection against sepsis, confirming the gut microbiota as a critical mediator. Furthermore, citrulline exhibited direct anti-inflammatory properties in cellular assays, and its plasma levels were negatively correlated with sepsis biomarkers (PCT and CRP) in patients. CONCLUSIONS:Taken together, our findings indicate that liraglutide mitigates sepsis by modulating the gut microbiota and regulating associated metabolic pathways. Citrulline may represent a potential microbial mediator or exploratory biomarker within this axis, warranting further mechanistic investigation.

AIMS:Despite its rising prevalence, older adults with Type 1 diabetes remain poorly characterised. This study aimed to characterise those over 60 years and quantify independent predictors of glycaemic control and complications. MATERIALS AND METHODS:We performed a multicentre analysis using real-world data from the China Diabetes Type 1 Study (CD1S), including 69 centres across China between 2016 and 2021. Patients were stratified into three age groups (≥ 60, 18-59 and < 18 years) for assessment of clinical characteristics and complications. Multiple regression examined associations of clinical features with HbA1c and complications. Variable importance was interpreted using SHAP values derived from a random forest model. RESULTS:We enrolled 9637 patients with Type 1 diabetes. Compared with young adults, older adults with Type 1 diabetes were less likely to achieve target HbA1c (7.2% vs. 10.2%, p < 0.05) and had a substantially higher burden of chronic complications, with 81.0% of older adults having at least one complication. Longer duration, higher BMI and higher gross domestic product (GDP) were significantly associated with lower HbA1c (all p < 0.05). Higher GDP was linked to reduced rates of diabetic retinopathy (19.7%), nephropathy (13.6%) and peripheral neuropathy (46.9%), yet a higher proportion of peripheral vasculopathy (41.8%) was observed compared with middle- and low-GDP regions. CONCLUSIONS:Older adults with Type 1 diabetes exhibit a unique clinical profile of poor glycaemic control and a high burden of complications, influenced by regional GDP disparities. This underscores the critical need to integrate socioeconomic factors into personalised care strategies to advance health equity. REGISTRATION NUMBER:NCT05498974.

Aims Data-driven clustering studies of type 2 diabetes (T2D) based on limited routine clinical variables have reproducibly described major metabolic subgroups, including severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes and mild age-related diabetes. We examined whether deeper metabolic phenotyping can improve phenotypic resolution beyond these low-dimensional frameworks.Methods We performed an exploratory cross-sectional clustering analysis in 94 adults with newly diagnosed T2D and complete data, drawn from an initial 97 screened participants who underwent deep metabolic phenotyping. Principal component analysis (PCA) guided selection of 13 relatively independent variables spanning six physiological domains: glycaemic exposure, insulin sensitivity and secretion, visceral adiposity, lipid metabolism, skeletal muscle status, hepatic injury and haematological indices. K-means clustering was applied to standardised variables. Robustness was assessed across prespecified 5-, 9- and 13-variable panels, exclusion of age, alternative muscle and adiposity representations and comparison with hierarchical and Gaussian mixture clustering. Autoimmune diabetes was not examined due to insufficient case numbers.Results Five phenotypic clusters were observed, including preserved insulin sensitivity with compensatory hyperinsulinaemia (IS-HI). Two clusters aligned with canonical SIDD-like and SIRD-like phenotypes. Within the enriched model, two additional physiologically coherent patterns were resolved: a myogenic-anaemia phenotype (MA), characterised by reduced skeletal muscle percentage and altered haematological indices and a hepato-lipotoxic phenotype (HL), marked by dyslipidaemia and elevated hepatic enzymes. In an unconstrained sensitivity rerun of the same 13-variable panel, however, a distinct SIDD-like cluster did not re-emerge, underscoring that higher-dimensional clustering depends in part on variable selection and biologically anchored interpretation. Internal robustness analyses supported preservation of the overall cluster architecture across variable panels, although alternative clustering methods yielded non-identical partitions.Conclusions Phenotypic depth may improve the mechanistic resolution of type 2 diabetes beyond routine-variable frameworks. However, the present single-centre findings are hypothesis-generating only, do not support clinical decision-making and all treatment-related inferences remain speculative without longitudinal outcome data and external validation.

Objective Diabetes mellitus is a chronic condition that requires constant blood glucose monitoring to prevent serious health risks. Accurate blood glucose prediction is essential for managing glucose fluctuations and reducing the risk of hypo- and hyperglycemic events. However, existing models often face limitations in prediction horizon and accuracy. This study aims to develop a hybrid deep learning model combining Transformer and Long Short-Term Memory (LSTM) networks to improve prediction accuracy and extend the prediction horizon, using personalized patient information and continuous glucose monitoring data to support better real-time diabetes management. Methods In this study, we propose a hybrid deep learning model combining Transformer and LSTM networks to predict blood glucose levels for up to 120 min. The Transformer Encoder captures long-range dependencies, while the LSTM models short-term patterns. To improve feature extraction, we integrate Bidirectional LSTM and Transformer Encoder layers at multiple stages. We also use positional encoding, dropout layers, and a sliding window technique to reduce noise and manage temporal dependencies. Richer features, including meal composition and insulin dosage, are incorporated to enhance prediction accuracy. The model's performance is validated using real-world clinical data and error grid analysis. Results On clinical data, the model achieved root mean square error/mean absolute error of 10.157/6.377 (30-min), 10.645/6.417 (60-min), 13.537/7.283 (90-min), and 13.986/6.986 (120-min). On simulated data, the results were 1.793/1.376 (15-min), 2.049/1.311 (30-min), and 3.477/1.668 (60-min). Clark Grid Analysis showed that over 96% of predictions fell within the clinical safety zone up to 120 min, confirming its clinical feasibility. Conclusion This study demonstrates that the combined Transformer and LSTM model can effectively predict blood glucose concentration in type 1 diabetes patients with high accuracy and clinical applicability. The model provides a promising solution for personalized blood glucose management, contributing to the advancement of artificial intelligence technology in diabetes care.

Aims: This study addressed the challenge of postprandial glycemic variability in type 1 diabetes (T1D), even with AID (automated insulin delivery). We evaluated the effectiveness of a non-carbohydrate counting (non-CC) meal bolus strategy in adults with T1D utilizing open-source AID. Methods: A total of 32 adults with T1D, aged 18 to 50 years, participated in a randomized crossover trial utilizing open-source AID. Following a 7-day run-in period, participants were randomly assigned to one of two groups: automatic mode (closed loop) or manual mode (open loop). After 2 weeks, the participants underwent a crossover to the alternate treatment mode. Prandial boluses were administered according to a sliding scale based on preprandial glucose levels, without utilizing either the exact carbohydrate content of meals or meal announcement buttons. The study compared the differences in time in range (TIR) and insulin dosage across the different phases. Results: Compared with the open-loop phase, the TIR for patients during the closed-loop phase increased significantly during the night (75.45% ± 12.08% vs. 83.05% ± 7.20%, P < 0.001) and 24 h (73.40% ± 9.98% vs. 79.21% ± 4.84%, P = 0.019), with a more pronounced effect observed at night. During the closed-loop phase, the frequency of 24-h hypoglycemic events (＜3.9 mmol/L) was reduced compared with the open-loop phase, with no difference in nocturnal hypoglycemic events. In addition, compared with the open-loop phase, there were no significant differences in average postprandial blood glucose and peak blood glucose levels during the closed-loop phase; however, the time to reach peak postprandial blood glucose was delayed (86.06 ± 20.80 min vs. 99.08 ± 15.05 min, P < 0.001). Conclusions: A non-CC meal bolus strategy based on preprandial glucose in adults with T1D utilizing open-source AID effectively prevents glycemic excursions and maintains a mean TIR over 70%.

Ferroptosis and mitochondrial metabolism are closely associated with the pathological processes of various diseases. However, the role of ferroptosis-related genes (FRGs) and mitochondrial metabolism-related genes (MMRGs) in poor ovarian response (POR) remains unexplored. First, transcriptome sequencing was conducted on ovarian granulosa cell samples from POR patients and controls. Candidate genes were screened through differential expression analysis and consensus clustering analysis. The biomarkers were subsequently screened using machine learning algorithms and receiver operating characteristic (ROC) curves. Nomogram construction and evaluation, enrichment analysis, drug prediction analysis, and molecular docking were subsequently carried out using the biomarkers. Finally, the expression of the biomarkers was authenticated using reverse transcription‒quantitative polymerase chain reaction (RT‒qPCR). PLA2G4B and PRKCG were identified as biomarkers by screening. The nomogram demonstrated that these two biomarkers could effectively predict the occurrence of POR. Gene set enrichment analysis (GSEA) revealed that PLA2G4B and PRKCG were both associated with terpenoid backbone biosynthesis. A TF-miRNA-mRNA network was constructed using the biomarkers. For PLA2G4B, dirithromycin had the highest score among the targeted drugs, whereas for PRKCG, bryostatins and enzastaurin had the highest scores. Molecular docking results indicated that their binding energies were less than − 5 kcal/mol. RT‒qPCR revealed that PLA2G4B and PRKCG were significantly upregulated in POR samples, which was consistent with the high-throughput sequencing results. PLA2G4B and PRKCG have been identified as potential mitochondrial- and ferroptosis-related biomarkers in POR, providing valuable insights for exploring the pathogenesis of POR. These findings may also aid in the development of new diagnostic and therapeutic strategies for POR.

This study aimed to investigate the role of LncRNA MALAT1 in pancreatic (3-cell dysfunction in type 2 diabetes mellitus (T2DM) and its mechanisms, particularly focusing on the Phospho-p38 signaling pathway. MIN6 cells were treated with palmitic acid (PA) to induce (3-cell dysfunction. Transcriptome analysis revealed upregulation of LncRNA MALAT1, which was further investigated for its effects on cell proliferation, apoptosis, ROS production, and insulin secretion. The involvement of the Phospho-p38 signaling pathway was explored through rescue experiments using DHC, an activator of Phospho-p38. In vivo validation was conducted using a T2DM rat model. PA treatment reduced cell proliferation, increased apoptosis, elevated ROS, and decreased insulin secretion. Knockdown of MALAT1 improved these dysfunctions and downregulated the Phospho-p38 pathway. In vivo studies confirmed the upregulation of MALAT1 and Phospho-p38 alongside significant (3-cell dysfunction. This study demonstrated that LncRNA MALAT1 played a crucial role in pancreatic (3-cell dysfunction in T2DM by activating the Phospho-p38 signaling pathway. Knockdown of MALAT1 mitigated PA-induced (3-cell damage by downregulating Phospho-p38, highlighting the MALAT1/Phospho-p38 axis as a potential therapeutic target for preserving pancreatic (3-cell function in T2DM. These findings provide new insights into the molecular mechanisms underlying T2DM and suggest novel avenues for intervention.

Objective:To assess the association between daily carbohydrate (CHO) intake and glycemic control in adults with type 1 diabetes (T1D). Methods:Patients with T1D who received continuous glucose monitoring (CGM) to manage their blood glucose levels were enrolled in the study. A dietitian analyzed dietary components, including carbohydrate, protein, and fat percentages in the total dietary intake. Mean individual daily CHO intake (MIDC) and relative deviation from MIDC (< 80% low; 81%-120% medium, >120% high CHO consumption) were compared with parameters of glycemic control assessed by CGM. Results:Records from 36 patients [11 male, 25 female; age 39.5 ± 13.9 years; HbA1c 9.0 ± 2.8% (75 ±31 mmol/mol)]. Provided 356 days of data for a total of 1,068 meals. Time in range (3.9-10 mmol/l) for low, medium, and high CHO consumption was 81.6 (70.96, 90.28)%, 74.65 (59.55, 84.9)%, and 64.58 (51.04, 77.78)%, respectively (P < 0.001). Time above range (>10 mmol/L) was 9.55 (1.39, 17.95)%, 10.42 (2.78, 27.43)%, and 27.08 (11.46, 47.92)%, respectively (P < 0.001). There was no between-group difference for time in hypoglycemia (< 3.9 mmol/L; P = 0.136). After adjusting for HbA1c, total calorie intake, and total daily insulin dose, carbohydrate intake was negatively correlated with achieving TIR ≥ 70%. Conclusions:Daily CHO intake was inversely associated with glycemic control in adults with T1D. A carbohydrate energy percentage between 40% and 50% and a relatively low daily carbohydrate intake may be a strategy to optimize glucose control in suboptimal-controlled T1D in real-world settings.

Glucagon-like peptide-1 receptor agonists (GLP-1RAs), initially developed for type 2 diabetes and obesity, have evolved into multi-organ potential therapeutics due to their pleiotropic effects beyond glycemic control. Mechanistically, GLP-1 signaling modulates immune and inflammatory pathways, regulates autophagy and pyroptosis, alleviates endoplasmic reticulum stress, and interacts with the gut microbiome. These pleiotropic effects provide a rationale for exploring their role in multiple organ systems. Clinical trials have demonstrated cardiovascular and renal protection, leading to additional approvals in high-risk populations. Early data also suggest potential benefits in liver disease, obstructive sleep apnea, chronic respiratory disorders, neurodegenerative and psychiatric conditions, reproductive dysfunction, obesity-associated cancers, and sepsis, although these remain investigational. Therefore, this review aims to synthesize the evidence on the mechanistic expansion of GLP-1RAs from metabolic regulators to systemic modulators of inflammation, autophagy, and organ protection, and explores their therapeutic repurposing across diseases.

BACKGROUND:The lack of specific predictors for type-2 diabetes mellitus (T2DM) severely impacts early intervention/prevention efforts. Elevated branched-chain amino acids (BCAAs: Isoleucine, leucine, valine) and aromatic amino acids (AAAs: Tyrosine, tryptophan, phenylalanine)) show high sensitivity and specificity in predicting diabetes in animals and predict T2DM 10-19 years before T2DM onset in clinical studies. However, improvement is needed to support its clinical utility. AIM:To evaluate the effects of body mass index (BMI) and sex on BCAAs/AAAs in new-onset T2DM individuals with varying body weight. METHODS:Ninety-seven new-onset T2DM patients (< 12 mo) differing in BMI [normal weight (NW), n = 33, BMI = 22.23 ± 1.60; overweight, n = 42, BMI = 25.9 ± 1.07; obesity (OB), n = 22, BMI = 31.23 ± 2.31] from the First People's Hospital of Yunnan Province, Kunming, China, were studied. One-way and 2-way ANOVAs were conducted to determine the effects of BMI and sex on BCAAs/AAAs. RESULTS:Fasting serum AAAs, BCAAs, glutamate, and alanine were greater and high-density lipoprotein (HDL) was lower (P < 0.05, each) in OB-T2DM patients than in NW-T2DM patients, especially in male OB-T2DM patients. Arginine, histidine, leucine, methionine, and lysine were greater in male patients than in female patients. Moreover, histidine, alanine, glutamate, lysine, valine, methionine, leucine, isoleucine, tyrosine, phenylalanine, and tryptophan were significantly correlated with abdominal adiposity, body weight and BMI, whereas isoleucine, leucine and phenylalanine were negatively correlated with HDL. CONCLUSION:Heterogeneously elevated amino acids, especially BCAAs/AAAs, across new-onset T2DM patients in differing BMI categories revealed a potentially skewed prediction of T2DM development. The higher BCAA/AAA levels in obese T2DM patients would support T2DM prediction in obese individuals, whereas the lower levels of BCAAs/AAAs in NW-T2DM individuals may underestimate T2DM risk in NW individuals. This potentially skewed T2DM prediction should be considered when BCAAs/AAAs are to be used as the T2DM predictor.

Postmenopausal osteoporosis (PMOP) is a common disease that endangers the health of elderly women. Cucumber seeds have shown excellent therapeutic effects on PMOP, but the mechanism of cucumber seed peptide (CSP) remains unclear. The expression levels of NF-κB and osteoclast-related genes were detected by RT-qPCR. The levels of apoptosis-related proteins were detected by Western blotting. Nuclear translocation of NF-κB p65 and osteoclast formation were detected by immunofluorescence and tartrate-resistant acid phosphatase (TRAP) staining, respectively. ELISA was used to detect the expression levels of OPG, M-CSF, and RANKL. Hematoxylin–eosin (H&E) and TRAP staining were used to observe the effects of CSP on bone formation. In RAW264.7 cells, CSP (0.4 mg/L, 4 mg/L, and 40 mg/L) effectively inhibited the expression of osteoclast-related genes (Cathepsin-K, MT1-MMP, MMP-9, and TRAP). TRAP-positive multinucleated giant cells gradually decreased. Furthermore, NF-κB pathway activation downstream of RANK was inhibited. In bone marrow stromal cells (BMSCs), the expression levels of M-CSF and RANKL gradually decreased, and OPG gradually increased with increasing CSP concentrations. Treatment of RAW264.7 cells with pyrrolidine dithiocarbamate (PDTC, an inhibitor of NF-κB) prevented the formation of osteoclasts. Treatment with different concentrations of CSP effectively decreased the levels of RANKL and M-CSF in rat serum and increased the expression of OPG in the oophorectomy (OVX) rat model. Furthermore, different concentrations of CSP could ameliorate the loss of bone structure and inhibit the formation of osteoclasts in rats. CSP inhibits osteoclastogenesis by regulating the OPG/RANKL/RANK pathway and inhibiting the NF-kB pathway.

Type 1 diabetes (T1D) is an increasingly common chronic disease, and its incidence continues to rise globally, posing huge challenges to the public health system. At the same time, due to the need for long-term strict diet control and blood sugar monitoring, the management process of diabetes is relatively complicated, which brings a lot of inconvenience to patients' daily life. Predicting blood glucose concentration (BGC) in T1D patients can help avoid abnormal blood glucose events and improve blood glucose management in T1D patients. However, accurate blood glucose prediction remains challenging because blood glucose levels are affected by multiple factors such as insulin injections, diet, and exercise. In this study a combined deep learning (DL) model of Transformer and long short-term memory network (LSTM) for blood glucose prediction in T1D patients is proposed, which fully integrates the advantages of Transformer and LSTM. For the proposed method Transformer captures sequence relationships globally, while LSTM focuses on local and long-term dependencies, thereby improving the overall performance of the model. In this study the clinical data set of 13 T1D patients for more than 4 weeks are provided by the First Renmin Hospital of Yunnan Province, and the 360-day blood glucose data set of 10 adult T1D patients generated by the UVa/Padova simulator. The prediction performance and clinical evaluation were performed using root mean square error (RMSE), mean absolute error (MAE), and Clark error grid analysis (EGA), respectively. The performance of the model on clinical data sets is 30-min prediction horizon (PH) (RMSE=10.157, MAE=6.377), 60-min PH (RMSE=10.645, MAE=6.417), 90-min PH (RMSE=13.537, MAE=7.283), 120-min PH (RMSE= 13.986, MAE= 6.986). The performance of the model on the simulated data set is 15-min PH (RMSE=1.793, MAE=1.376), 30-min PH (RMSE=2.049, MAE=1.311), 60-min PH (RMSE=3.477, MAE=1.668). The units of RMSE and MAE are mg/dl. In addition, Clark Grid Analysis (EGA) analysis showed that more than 96% of BGC predictions remained within the clinical safety zone during the PH period of up to 120 minutes in the clinical data set, validating its clinical feasibility.

Autoimmune responses are the most important pathogenic mechanisms underlying type 1 diabetes (T1D). Extracellular vesicles (EVs) derived from mesenchymal stem cells (MSCs) have immunomodulatory effects. In this study, we investigated whether EVs derived from human umbilical cord MSCs (HucMSC-EVs) have treatment effects on nonobese diabetic (NOD) mice as model of T1D. HucMSC-EVs were isolated from human umbilical cord MSCs and characterized. NOD mice (aged 4 weeks) were administered with HucMSC-EVs or the same volume of phosphate-buffered saline (PBS) via caudal vein injection twice per week. After 8 weeks of treatment, blood, spleen, and pancreatic samples were collected. Mouse blood glucose levels and body weights were measured during treatment, and insulin concentration and inflammatory cytokine levels were analyzed by enzyme-linked immunosorbent assay (ELISA). Hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC) staining were used to evaluate pathological changes in mouse islets. T lymphocyte subsets were evaluated by flow cytometry, while quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot (WB) analyses were used to detect the expression of transcription factor and inflammatory cytokines. Our data indicated that HucMSC-EVs treatment reduced blood glucose levels and increased insulin concentration in NOD mice. Levels of interleukin-2 (IL-2), IL-4, and IL-10 were significantly increased and those of IL-1β and interferon-γ (IFN-γ) significantly decreased in the HucMSC-EVs group. The positive ratio of CD4+ T lymphocyte subsets decreased after intravenous injection of HucMSC-EVs, in which the proportion of Th2 cells increased and that of Th1 decreased. GATA-3 and IL-2, IL-4 and IL-10 expression levels were upregulated in spleen on treatment with HucMSC-EVs, whereas those of T-bet and IFN-γ were downregulated. In addition, more inflammatory cell infiltration was detected in the pancreas of control group mice than those treated with HucMSC-EVs. IHC staining showed that Fas/FasL expression and distribution in control group pancreas were higher than those in the HucMSC-EVs group. Together, our findings indicate that HucMSC-EVs have potential to prevent islet injury via T cell immune responses by adjusting the Th1/Th2 ratio to regulate secretion of inflammatory factors.

ObjectivesPatients with type 1 diabetes (T1D) face unique challenges in glycaemic control due to the complexity and uniqueness of the dietary structure in China, especially in terms of postprandial glycaemic response (PPGR). This study aimed to establish a personalized model for predicting PPGR in patients with T1D.Materials and methodsData provided by the First People’s Hospital of Yunnan Province, 13 patients with T1D, were recruited and provided with an intervention for at least two weeks. All patients were asked to wear a continuous glucose monitoring (CGM) device under free-living conditions during the study period. To tackle the challenge of incomplete data from wearable devices for CGM measurements, the GAIN method was used in this paper to achieve a more rational interpolation process. In this study, patients’ PPGRs were calculated, and a LightGBM prediction model was constructed based on a Bayesian hyperparameter optimisation algorithm and a random search algorithm, which integrated glucose measurement, insulin dose, dietary nutrient content, blood measurement and anthropometry as inputs.ResultsThe experimental outcomes revealed that the PPGR prediction model presented in this paper demonstrated superior accuracy (R=0.63) compared to both the carbohydrate content only model (R=0.14) and the baseline model emulating the standard of care for insulin administration (R=0.43). In addition, the interpretation of the model using the SHAP method showed that blood glucose levels at meals and blood glucose trends 30 minutes before meals were the most important features of the model.ConclusionThe proposed model offers a heightened precision in predicting PPGR in patients with T1D, so it can better guide the diet plan and insulin intake dose of patients with T1D.

AIMS:To evaluate the status quo of type 1 diabetes (T1D) management and characteristics of hospitalised patients with T1D in China through a nationwide multicentre registry study, the China Diabetes Type 1 Study (CD1S).MATERIALS AND METHODS:Clinical data from the electronic hospital records of all people with T1D were retrospectively collected in 13 tertiary hospitals across 7 regions of China from January 2016 to December 2021. Patients were defined as newly diagnosed who received a diagnosis of diabetes for less than 3 months.RESULTS:Among the 4993 people with T1D, the median age (range) at diagnosis was 23.0 (1.0-87.0) years and the median disease duration was 2.0 years. The median haemoglobin A1c (HbA1c) level was 10.7%. The prevalence of obesity, overweight, dyslipidemia, and hypertension were 2.5%, 10.8%, 62.5% and 25.9%, respectively. The incidence rate of diabetic ketoacidosis at disease onset was 41.1%, with the highest in children <10 years of age (50.6%). In patients not newly diagnosed, 60.7% were diagnosed with at least one chronic diabetic complication, with the highest proportion (45.3%) of diabetic peripheral neuropathy. Chronic complications were detected in 79.2% of people with T1D duration ≥10 years.CONCLUSIONS:In the most recent years, there were still unsatisfactory metabolic control and high incidence of diabetic ketoacidosis as well as chronic diabetic complications among inpatients with T1D in China. The ongoing CD1S prospective study aims to improve the quality of T1D management nationally.

Type 1 diabetes mellitus (T1DM) is well-known to trigger a disruption of lipid metabolism. This study aimed to compare lipid profile changes in T1DM patients after achieving glucose control and explore the underlying mechanisms. In addition, we seek to identify novel lipid biomarkers associated with T1DM under conditions of glycemic control. A total of 27 adults with T1DM (age: 34.3 ± 11.2 yrs) who had maintained glucose control for over a year, and 24 healthy controls (age: 35.1 + 5.56 yrs) were recruited. Clinical characteristics of all participants were analyzed and plasma samples were collected for untargeted lipidomic analysis using mass spectrometry. We identified 594 lipid species from 13 major classes. Differential analysis of plasma lipid profiles revealed a general decline in lipid levels in T1DM patients with controlled glycemic levels, including a notable decrease in triglycerides (TAGs) and diglycerides (DAGs). Moreover, these T1DM patients exhibited lower levels of six phosphatidylcholines (PCs) and three phosphatidylethanolamines (PEs). Random forest analysis determined DAG(14:0/20:0) and PC(18:0/20:3) to be the most prominent plasma markers of T1DM under glycemic control (AUC = 0.966). The levels of all metabolites from the 13 lipid classes were changed in T1DM patients under glycemic control, with TAGs, DAGs, PCs, PEs, and FFAs demonstrating the most significant decrease. This research identified DAG(14:0/20:0) and PC(18:0/20:3) as effective plasma biomarkers in T1DM patients with controled glycemic levels.

PURPOSE:Pancreatic β-cells play a critical role in regulating plasma insulin levels and glucose metabolism balance, with their dysfunction being a key factor in the progression of diabetes. This review aims to explore the role of autophagy, a vital cellular self-maintenance process, in preserving pancreatic β-cell functionality and its implications in diabetes pathogenesis. METHODS:We examine the current literature on the role of autophagy in β-cells, highlighting its function in maintaining cell structure, quantity, and function. The review also discusses the effects of both excessive and insufficient autophagy on β-cell dysfunction and glucose metabolism imbalance. Furthermore, we discuss potential therapeutic agents that modulate the autophagy pathway to influence β-cell function, providing insights into therapeutic strategies for diabetes management. RESULTS:Autophagy acts as a self-protective mechanism within pancreatic β-cells, clearing damaged organelles and proteins to maintain cellular stability. Abnormal autophagy activity, either overactive or deficient, can disrupt β-cell function and glucose regulation, contributing to diabetes progression. CONCLUSION:Autophagy plays a pivotal role in maintaining pancreatic β-cell function, and its dysregulation is implicated in the development of diabetes. Targeting the autophagy pathway offers potential therapeutic strategies for diabetes management, with agents that modulate autophagy showing promise in preserving β-cell function.