Risk Of Hypoglycemia In Patients Research Articles

Chitosan hydrochloride coated and nonionic surfactant modified niosomes: a better way for oral administration of semaglutide.

Diabetes is now a global chronic disease, with the number of people with diabetes expected to reach 643 million by the end of 2030. Semaglutide, a human glucagon-like peptide-1 (GLP-1) analogue with 94% similarity to human GLP-1, can promote insulin secretion and repress glucagon secretion in a glucose concentration-dependent manner, resulting in substantial improvement of blood glucose levels and reducing the risk of hypoglycemia in patients with type 2 diabetes. To improve the absorption efficiency of semaglutide in oral delivery, we developed chitosan hydrochloride-coated and nonionic surfactant-modified niosomes (CS.HCL-NSPEs-NIO) as a new way to encapsulate it. The results showed that CS.HCL-NSPEs-NIO could efficiently penetrate the cell junctions in the intestinal endothelium and therefore promote drug absorbance. In addition, gastrointestinal distribution studies revealed that CS. HCL-NSPEs-NIO could stay in the intestine for more than 4 hours, thus allowing for long-term glucose regulation. Effective reduction of blood glucose levels and weight loss were observed in db/db mice while no toxicity was detected in major organs. Overall, we suggest CS.HCL-NSPEs-NIO is a promising oral delivery agent to improve the hypoglycemic effect of semaglutide.

Update on the Efficacy and Safety of Sodium–Glucose Co-Transporter 2 Inhibitors in Patients with Chronic Diseases: A Systematic Review and Meta-Analysis

Background: Sodium–glucose co-transporter-2 (SGLT2) inhibitors have emerged as vital medications for the management of type 2 diabetes mellitus (T2DM). Numerous studies have highlighted the cardioprotective and renal protective benefits of SGLT2 inhibitors. Consequently, it is essential to assess their efficacy and safety in patients with chronic diseases. Method: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) evaluating the effects of SGLT2 inhibitors on major cardiovascular and safety outcomes in patients with T2DM, heart failure (HF), and chronic kidney disease (CKD). We searched the PubMed, Cochrane, and Embase databases for trials published between 30 September 2021 and 17 May 2023. The primary outcomes of interest included nonfatal myocardial infarction (MI), hospitalization for heart failure (HHF), cardiovascular death, and nonfatal stroke. The safety outcomes assessed were hypoglycemia, urinary tract infections (UTIs), and acute kidney injury (AKI). Result: We identified 13 RCTs involving 90,413 participants. In patients with T2DM, SGLT2 inhibitors significantly reduced the risk of nonfatal MI by 12% (hazard ratio [HR] = 0.88, 95% confidence interval [CI]: 0.78–0.98), HHF by 33% (HR = 0.67, 95% CI: 0.62–0.74), and cardiac death by 15% (HR = 0.95, 95% CI: 0.80–1.13). However, they did not significantly reduce the risk of nonfatal stroke (HR = 0.85, 95% CI: 0.75–0.95). In patients with HF, SGLT2 inhibitors reduced the risk of HHF by 28% (HR = 0.72, 95% CI: 0.66–0.77) and cardiac death by 12% (HR = 0.88, 95% CI: 0.80–0.96). For patients with CKD, SGLT2 inhibitors reduced the risk of HHF by 35% (HR = 0.65, 95% CI: 0.55–0.76) and cardiac death by 16% (HR = 0.84, 95% CI: 0.73–0.96). Regarding safety outcomes, SGLT2 inhibitors did not significantly increase the risk of hypoglycemia in patients with T2DM, HF, or CKD, nor did they increase the risk of urinary tract infections (UTIs) in patients with HF or CKD, or the risk of acute kidney injury (AKI) in patients with HF. However, they did increase the risk of UTIs by 8% (risk ratio [RR] = 1.08, 95% CI: 1.01–1.16) in patients with T2DM and reduced the risk of AKI by 22% (RR = 0.78, 95% CI: 0.67–0.89) and 19% (RR = 0.81, 95% CI: 0.69–0.97) in patients with T2DM and CKD, respectively. Conclusions: SGLT2 inhibitors have demonstrated a significant improvement in cardiovascular outcomes for patients with T2DM, HF, and CKD while also maintaining a favorable safety profile. These findings advocate for the broader application of SGLT2 inhibitors in the management of chronic diseases, particularly in reducing the incidence of nonfatal MI, HHF, and cardiac death. Further research is essential to optimize their use across diverse patient populations and stages of disease.

Increased Risk of Hypoglycemia Following Roux-en-Y Gastric Bypass Surgery in Patients Without Diabetes: a Propensity Score-Matched Analysis

BackgroundRoux-en-Y gastric bypass (RYGB) surgery is an effective treatment for obesity. However, the incidence and long-term risk of hypoglycemia after surgery in patients without diabetes remains unclear. This study aimed to investigate the prevalence of hypoglycemia following RYGB surgery in patients with obesity and without diabetes.MethodsA retrospective cohort study was conducted using the TriNetX database. The study population included 15,085 patients with obesity (BMI ≥ 30 kg/m2) who underwent RYGB surgery and 3,200,074 non-surgical controls, all without a history of diabetes or GLP-1 receptor agonist use. Propensity score matching was performed to balance baseline characteristics. The primary outcome was the incidence of hypoglycemia, defined by ICD-10-CM codes or laboratory values (glucose ≤ 70 mg/dL). Cox regression analysis was employed to calculate hazard ratios (HR) and 95% confidence intervals (CI).ResultsIn the overall study population, the risk of hypoglycemia was significantly higher in the RYGB group (18.70%, n = 2,810) compared to the control group (3.80%, n = 120,923; HR 4.3, 95% CI 4.14–4.46, p < 0.001). After propensity score matching (n = 14,916 per group), RYGB patients maintained an elevated risk (18.70%, n = 2,795) compared to matched controls (5.0%, n = 749; HR 3.7, 95% CI 3.44–4.05, p < 0.001). Time-series analysis revealed consistently higher hypoglycemia risk in the RYGB group, with hazard ratios ranging from 5.37 (95% CI 4.09–7.03) at 1 week to 3.75 (95% CI 3.45–4.06) at 10 years post-surgery (all p < 0.001). Subgroup analysis of RYGB patients who developed hypoglycemia showed a 30-day hospitalization rate of 21.3% and a mortality rate of 0.71%.ConclusionRYGB surgery is associated with a significantly increased risk of hypoglycemia in patients with obesity and without diabetes, both in the short-term and long-term follow-up. These findings underscore the importance of monitoring and managing hypoglycemia in patients undergoing RYGB surgery, even in the absence of preexisting diabetes.Graphical

Retrospective Analysis Comparing the Efficacy of GLP-1 Receptor Agonists Dulaglutide and Semaglutide

Background: Glucagon-like peptide-1 receptor agonists have gained popularity in recent years because of their well-established benefits of increased glycemic control, weight loss, cardiovascular protection, and renal protection, with minimal adverse effects, and low risk for hypoglycemia in patients with type 2 diabetes. This study explored the effectiveness of the GLP-1 receptor agonist semaglutide and dulaglutide on body weight and hemoglobin A1c in patients with type 2 diabetes. Methods: In this retrospective cohort study, 244 patients diagnosed with type 2 diabetes and prescribed semaglutide or dulaglutide were identified through Lourdes Hospital electronic medical records. Over a 12-month period, in set intervals every 3 months, patients were assessed for changes in their hemoglobin A1c and body weight measured as a reduction in their body mass index (BMI). These primary outcomes were compared using linear mixed models from the lme4 package in R (v. 4.2.2). Results: Semaglutide and dulaglutide showed comparable effectiveness in reducing hemoglobin A1c compared with baseline. Both medications also led to a reduction in BMI. However, semaglutide showed statistically significant improvements in weight loss compared with dulaglutide at 3-6 months ( P = 0.01), 6-9 months ( P = 0.01), and 9-12 months ( P = 0.01). Conclusions: These findings suggest that in type 2 diabetes, semaglutide and dulaglutide show comparable effectiveness in reducing hemoglobin A1c. However, semaglutide was shown to be more effective in promoting weight loss. While these findings may be beneficial when treating patients with type 2 diabetes and obesity, treatment should be individualized to the patient, considering all factors before treatment choice.

AGE, DISEASE DURATION AND MULTIMORBIDITY AS PREDICTORS OF HYPOGLYCEMIA IN ELDERLY AND SENILE WOMEN WITH TYPE 2 DIABETES MELLITUS

AbstractAim – to study the relationship between age, duration of the disease and concomitant chronic non-infectious pathology with the risk of developing hypoglycemia in patients with type 2 diabetes mellitus (T2DM).Material and methods. The study involved 90 elderly and senile women (mean age 70.5 ± 6.2 years) with T2DM. A detailed history of the disease was collected from all study participants, and medical documentation was studied. The indicators of clinical blood test and biochemical blood test were studied, and the Charlson comorbidity index was calculated.Results. The prevalence of hypoglycemia among patients with T2DM was: 47% in the elderly and 75% in senile patients. The correlation index between the indicators “Patient’s age” and “Presence of hypoglycemia” was r = 0.2489 (p = 0.018), which indicates a statistical dependence of these values. When calculating χ2 (chi-square), the value obtained was χ2 = 5.513 (p = 0.018). One-way analysis of variance on these values shows a result of F-ratio = 5.811 at the significance level (p = 0.018), which confirms a significant relationship between the two variables. The correlation index was calculated for the indicators “Presence of cases of hypoglycemia” and “Existence of diabetes mellitus” (r = 0.3512 with a significance level of p = 0.0007). The data allows us to draw a conclusion about the statistical dependence of these values. The result of the χ2 test for trend was the value χ2 (trend) = 10.982, (p = 0.0009). The data presented indicate the relationship between these variables. The correlation between the indicators “Presence of cases of hypoglycemia” and “Charlson Comorbidity Index (scores)” is confirmed by the value r = 0.4020 (p = 0.0001). The relationship between these variables was revealed by calculating χ2 = 16.336, (p = 0.0059). Based on the χ2 test for trend, the value χ2 (trend) = 14.544, (p = 0.0001) was obtained. One-way analysis of variance for these indicators shows the result of F-ratio = 3.734 (p = 0.004).Conclusion. The patient's age, duration of T2DM and multimorbidity are significantly associated with the risk of hypoglycemia in patients with T2DM.

The opportunities and challenges of the disease-modifying immunotherapy for type 1 diabetes: A systematic review and meta-analysis

There are multiple disease-modifying immunotherapies showing the potential of preventing or delaying the progression of type 1 diabetes (T1D). We designed and performed this systematic review and meta-analysis to gain an overview of what a role immunotherapy plays in the treatment of T1D. We searched PubMed, Embase and Cochrane Central Register of Controlled Trials (CENTRAL) from inception to December 2023. We included clinical trials of immunotherapy conducted in patients with T1D that reported the incidence of hypoglycemia or changes from baseline in at least one of following outcomes: 2 h and 4 h mixed-meal-stimulated C-peptide area under the curve (AUC), fasting C-peptide, daily insulin dosage, glycated hemoglobin (HbA1c) and fasting plasma glucose (FPG). The results were computed as the weighted mean differences (WMDs) or odds ratios (ORs) and 95% confidence intervals (CIs) in random-effect model. In all, 34 clinical trials were included. When compared with control groups, 2 h C-peptide AUC was marginally higher in patient treated with nonantigen-based immunotherapies (WMD, 0.04nmol/L, 95% CI, 0.00–0.09 nmol/L, P=0.05), which was mainly driven by the effects of T cell-targeted therapy. A greater preservation in 4 h C-peptide AUC was observed in patients with nonantigen-based immunotherapies (WMD, 0.10nmol/L, 95% CI, 0.04–0.16 nmol/L, P=0.0007), which was mainly driven by the effects of tumor necrosis factor α (TNF-α) inhibitor and T cell-targeted therapy. After excluding small-sample trials, less daily insulin dosage was observed in patient treated with nonantigen-based immunotherapies when compared with control groups (WMD, −0.07units/kg/day, 95% CI, −0.11 to −0.03units/kg/day, P=0.0004). The use of antigen-based immunotherapies was also associated with a lower daily insulin dosage versus control groups (WMD, −0.11units/kg/day, 95% CI, −0.23 to −0.00units/kg/day, P=0.05). However, changes of HbA1c or FPG were comparable between nonantigen-based immunotherapies or antigen-based immunotherapies and control groups. The risk of hypoglycemia was not increased in patients treated with nonantigen-based immunotherapies or patients treated with antigen-based immunotherapies when compared with control groups. In conclusion, nonantigen-based immunotherapies were associated with a preservation of 2 h and 4 h C-peptide AUC in patients with T1D when compared with the controls, which was mainly driven by the effects of TNF-a inhibitor and T cell-targeted therapy. Both nonantigen-based immunotherapies and antigen-based immunotherapies tended to reduce the daily insulin dosage in patients with T1D when compared with the controls. However, they did not contribute to a substantial improvement in HbA1c or FPG. Both nonantigen-based immunotherapies and antigen-based immunotherapies were well tolerated with not increased risk of hypoglycemia in patients with T1D.

Development and validation of a prediction model for self-reported hypoglycemia risk in patients with type 2 diabetes: A longitudinal cohort study.

To develop and validate a simple prediction model for hypoglycemia risk in patients with type 2 diabetes. We prospectively analyzed the data of 1,303 subjects in a third-class hospital in Tianjin and followed up their hypoglycemia events at 3 and 6 months. The hypoglycemia risk prediction models for 3 and 6 months were developed and the model performance was evaluated. A total of 340 (28.4%) patients experienced hypoglycemia within 3 months and 462 (37.2%) within 6 months during the follow-up period. Age, central obesity, intensive insulin therapy, frequency of hypoglycemia in the past year, and hypoglycemia prevention education entered both model3month and model6month. The area under the receiver operating characteristic curve of model3month and model6month were 0.711 and 0.723, respectively. The Youden index was 0.315 and 0.361, while the sensitivities were 0.615 and 0.714, and the specificities were 0.717 and 0.631. The calibration curves showed that the models were similar to reality. The decision curves implied that the clinical net benefit of the model was clear. The study developed 3 and 6 month hypoglycemia risk prediction models for patients with type 2 diabetes. The discrimination and calibration of the two prediction models were good, and might help to improve clinical decision-making and guide patients to more reasonable self-care and hypoglycemia prevention at home.

Development and Validation of a Machine Learning Model to Predict Weekly Risk of Hypoglycemia in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring.

Aim: The aim of this study was to develop and validate a prediction model based on continuous glucose monitoring (CGM) data to identify a week-to-week risk profile of excessive hypoglycemia. Methods: We analyzed, trained, and internally tested two prediction models using CGM data from 205 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach (XGBoost) combined with feature engineering deployed on the CGM signals was utilized to predict excessive hypoglycemia risk defined by two targets (time below range [TBR] >4% and the upper TBR 90th percentile limit) of TBR the following week. The models were validated in two independent cohorts with a total of 253 additional patients. Results: A total of 61,470 weeks of CGM data were included in the analysis. The XGBoost models had an area under the receiver operating characteristic curve (ROC-AUC) of 0.83-0.87 (95% confidence interval; 0.83-0.88) in the test dataset. The external validation showed ROC-AUCs of 0.81-0.90. The most discriminative features included the low blood glucose index, the glycemic risk assessment diabetes equation (GRADE), hypoglycemia, the TBR, waveform length, the coefficient of variation and mean glucose during the previous week. This highlights that the pattern of hypoglycemia combined with glucose variability during the past week contains information on the risk of future hypoglycemia. Conclusion: Prediction models based on real-world CGM data can be used to predict the risk of hypoglycemia in the forthcoming week. The models showed good performance in both the internal and external validation cohorts.

Triple therapy for Helicobacter pylori eradication and the risk of hypoglycemia in patients with diabetes: a population-based cohort study

The incidence of type 2 diabetes mellitus has risen globally, from 108 million cases in 1980 to 422 million cases in 2014. Although controlling glycemic levels in patients with diabetes is crucial, insulin and sulfonylureas can cause hypoglycemic episodes and even potentially fatal events such as comas, seizures, life-threatening arrhythmias, and myocardial infarctions. Several antibiotics have been documented to cause hypoglycemic episodes; the use of antibiotics along with insulin or sulfonylureas might further increase the risk of hypoglycemia. Therefore, researchers must determine which antibiotics carry a risk of inducing severe hypoglycemic events. The prevalence of H. pylori infection remains high in most countries, and the infection is often treated with triple therapy involving amoxicillin, clarithromycin, and a proton pump inhibitor (PPI). Several case reports have reported that hypoglycemia can occur when used with patients who also take diabetes medication. Therefore, we hypothesized that patients with diabetes have an increased risk of hypoglycemic episodes when being treated with triple therapy for H. pylori infection. By analyzing medical records from Taiwan’s National Health Insurance Research Database, we found a significant association between hypoglycemia and triple therapy treatment for diabetic patients with peptic ulcer disease. Prescribing triple therapy to patients with diabetes and peptic ulcers significantly increased the risk of a hypoglycemic episode (adjusted odds ratio [aOR] = 1.75, 95% confidence interval [CI]: 1.64 to 1.88, P < 0.001). Similarly, the highest aOR (5.77, 95% CI 4.82 to 6.92) was found in patients with diabetes and peptic ulcers who had hypoglycemic episodes within 30 days after triple therapy treatment. Many patients with diabetes require H.pylori eradication for peptic ulcer treatment, and vigilance toward the risk of hypoglycemia in this population is thus necessary.

SAFETY OF HYPOGLYCEMIC THERAPY IN CASES OF PATIENTS WITH POLYMORBID PATHOLOGIES

Impairment of the excretory function of the kidneys was revealed in 69% of patients with type 2 diabetes admitted to the hospital in therapeutic departments. A decrease of GFR below 30 ml/min/1,73 m2, which can significantly affect the choice and dosage regimen of hypoglycemic drugs, was recorded in 35% of patients. According to the anamnesis, sulfonylurea derivatives took 66.3% of hospitalized patients, biguanides in monotherapy - 25%, a combination of drugs from these two groups was prescribed in 35% cases. According to the analysis of the pharmacotherapy, 8.2% of patients with type 2 diabetes mellitus required correction of hypoglycemic therapy because of the functional state of the kidneys. According to the results of laboratory research methods , during hospitalization 3% of patients had episodes of hypoglycemia ( blood glucose level 3.9 mmol/L) or boundary values of glucose (blood glucose level = 4.0-4.1 mmol/L), but endocrinologist consultations and drug therapy correction hadn't been carried out. Therefore the lack of care of 'non-endocrinologists' regarding the risk of hypoglycemia in patients with type 2 diabetes with renal dysfunction leads to the absence of pharmacotherapy correction during hospitalization.

Diet and exercise are a fundamental part of comprehensive care for type 2 diabetes.

Diet and exercise are a fundamental part of comprehensive care for type 2 diabetes.

148-OR: CGM Time Below Range Predicts Epinephrine Response to Hypoglycemia in Patients with Type 1 Diabetes

Identifying T1D patients with impaired awareness of hypoglycemia (IAH) is essential, as these patients are at ~10-fold risk of severe hypoglycemia. Defining IAH has relied on questionnaires such as Clarke score that are rarely used in clinical practice. Additional tools are needed to stratify hypoglycemia risk in patients with T1D. Here we show that CGM metrics correlate with physiologic response to hypoglycemia. We measured serum epinephrine levels in 22 subjects with T1D (ages 28-52, A1c 6.1-7.2, median Clarke score 3) during a hyperinsulinemic-hypoglycemic clamp. Baseline CGM data was collected for 2 weeks prior to clamp. These data show a novel, inverse relationship between CGM time below range (TBR, &amp;lt;70 mg/dL) and epinephrine response (EpiR, pg/mL/min) FIGURE. CGM TBR (ρ = -0.562, p = 0.006), Clarke score (ρ = -0.592, p = 0.03), and older age (ρ = -0.500, p = 0.015) were inversely associated with EpiR. Higher average glucose was associated with increased EpiR while time in range, coefficient of variation, BMI, and duration of T1D showed no significant association with EpiR. Our study suggests that CGM TBR predicts counterregulatory dysfunction in T1D. CGM TBR demonstrated a robust, inverse correlation with EpiR similar to the validated Clarke survey. CGM is now the gold standard for glycemic monitoring in T1D, and the percentage TBR may help identify patients at highest risk for severe hypoglycemia outcomes. Disclosure R.L.Thomas: None. J.M.Gregory: Advisory Panel; Eli Lilly and Company, vTv Therapeutics, Other Relationship; Medtronic. E.R.Giovannetti: None. S.C.Boeder: Consultant; Cecelia Health, Research Support; Dexcom, Inc., REMD Biotherapeutics, Eli Lilly and Company. J.Pettus: Advisory Panel; Sanofi, Novo Nordisk, Lilly, MannKind Corporation, Consultant; Carmot Therapeutics, Inc., Diasome. Funding JDRF (2-SRA-2018-606-M-B, 5-ECR-2020-950-A-N); National Institute of Diabetes and Digestive and Kidney Diseases (K23DK123392)

Associations of Colonoscopy and Risk of Hypoglycemia in Patients with Type 2 Diabetes.

Inpatients undergoing colonoscopy may be at increased risk of hypoglycemia. However, few high-quality studies have examined the relationship between hypoglycemia and colonoscopy in patients with type 2 diabetes mellitus (T2DM). A total of 1016 patients from a large tertiary hospital were enrolled in this retrospective study. We collected demographic information, laboratory indices, colonoscopy information and hypoglycemia information from the enrolled patients during hospitalization. Logistic regression analysis was adopted to estimate the adjusted odds ratios to determine the association between hypoglycemia and colonoscopy. Hypoglycemia occurred in 80 of 788 (10.1%) patients without colonoscopy exposure and 37 of 228 (16.2%) patients with colonoscopy exposure. 25 patients (67.6%) had hypoglycemic events from 3 hours to 68 hours after the end of colonoscopy. Adjusting for demographic and clinical covariates, the risk of hypoglycemia was 1.99 times higher in those who underwent colonoscopy than in those who did not (OR 1.99, 95% CI 1.25-3.19). The association was consistent in subgroups of females, the elderly, the overweight patients, patients with long duration of disease or patients with suboptimal glycemic control. A strong association between colonoscopy and an increased risk of hypoglycemia is observed in patients with T2DM. When performing a colonoscopy for diabetics, the risk of hypoglycemia should be considered even within 68 hours after colonoscopy.

IDF2022-1243 Risk of hypoglycemia in patients taking antidiabetic drugs and concurrent oral anticoagulants: A nationwide analysis

IDF2022-1243 Risk of hypoglycemia in patients taking antidiabetic drugs and concurrent oral anticoagulants: A nationwide analysis

Network meta-analysis of glucose-lowering drug treatment regimens with the potential risk of hypoglycemia in patients with type 2 diabetes mellitus in terms of glycemic control and severe hypoglycemia.

Insulin and its secretagogues are essential for some patients with type 2 diabetes (T2D) to maintain good glycemic control (GC), but severe hypoglycemia (SH) is a concern. This network meta-analysis aimed to find optimal glucose-lowering drug treatment regimens in terms of GC and SH in T2D patients. MEDLINE and EMBASE were used to identify trials that compared two or more treatments including insulins and/or sulfonylurea or glinides and that examined both GC and SH. Treatment hierarchy was expressed as the surface under the cumulative ranking curve (SUCRA) probabilities. We identified 137 eligible trials comprising 42 treatments. The use of insulins and non-insulin glucose-lowering agents except for sulfonylurea or glinide had a higher SUCRA than insulins only for hemoglobin A1c (A1C) (p = 0.01) changes and achievement of A1C < 7.0% (p = 0.02) or A1C ≤ 6.5% (p = 0.002). The use of sulfonylurea or glinide and other non-insulin glucose-lowering agents resulted in a lower SUCRA for SH than insulins only when trials were analyzed for A1C change (p = 0.06) and achievement of A1C < 7.0% (p = 0.004) or A1C ≤ 6.5% (p = 0.004). Cluster analysis indicated that premixed insulin plus glucagon-like peptide-1 receptor agonist (Mix-ins + GLP1) belonged to the high-efficacy category for GC and glinide plus thiazolidinedione (glinide + TZD) belonged to the relatively high-efficacy category for GC among several high-safety categories regarding SH. In T2D patients, clinicians should consider appropriate combinations of non-insulin glucose-lowering agents (especially glinide + TZD) for reducing SH risk before switching to insulin therapies. If switching, they should be willing to add non-insulin glucose-lowering agents (especially, Mix-ins + GLP1) to insulins to further improve GC.

The Risk of Severe Hypoglycemia and Mortality in Patients With Type 2 Diabetes and Discharged With Acute Liver Injury.

To compare the risks of severe hypoglycemia and mortality between patients with type 2 diabetes (T2D) and discharged with and without acute liver injury. From 1 January 2000 to 31 December 2010, we identified patients with T2D and hospitalization for acute liver injury and hospitalization for other causes from the National Health Insurance Research Database of Taiwan. Multivariable-adjusted Cox proportional hazards models were used to compare the risks of severe hypoglycemia and mortality between the study and control groups. The incidence rates and adjusted hazard ratios (aHRs) for severe hypoglycemia within 90 days and 365 days after discharge were 12.28 and 5.59/1,000 person-years (aHR 1.92 [1.30-2.85]) and 7.35 and 2.9/1,000 person-years (aHR 1.98 [1.52-2.58]) for patients discharged with and without acute liver injury, respectively. The incidence rates and aHRs for mortality within 90 days and 365 days after discharge were 82.4 and 27.54/1,000 person-years (aHR 1.73 [1.46-2.05]) and 36.8 and 9.3/1,000 person-years (aHR 1.94 [1.69-2.24]) for patients discharged with and without acute liver injury, respectively. The subgroup analysis of hypoglycemia risk in patients discharged with acute liver injury revealed no significant interaction in risk factors of age, chronic kidney disease, and medications, except for sex difference, which has significant interaction. This cohort study demonstrated that patients with T2D and discharged with acute liver injury showed significantly higher risks of severe hypoglycemia and mortality within 90 days and 365 days after discharge than patients discharged with other causes.

Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record-Based Machine Learning: Development and Validation.

BackgroundHypoglycemia is a common adverse event in the treatment of diabetes. To efficiently cope with hypoglycemia, effective hypoglycemia prediction models need to be developed.ObjectiveThe aim of this study was to develop and validate machine learning models to predict the risk of hypoglycemia in adult patients with type 2 diabetes.MethodsWe used the electronic health records of all adult patients with type 2 diabetes admitted to West China Hospital between November 2019 and December 2021. The prediction model was developed based on XGBoost and natural language processing. F1 score, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA) were used as the main criteria to evaluate model performance.ResultsWe included 29,843 patients with type 2 diabetes, of whom 2804 patients (9.4%) developed hypoglycemia. In this study, the embedding machine learning model (XGBoost3) showed the best performance among all the models. The AUC and the accuracy of XGBoost are 0.82 and 0.93, respectively. The XGboost3 was also superior to other models in DCA.ConclusionsThe Paragraph Vector–Distributed Memory model can effectively extract features and improve the performance of the XGBoost model, which can then effectively predict hypoglycemia in patients with type 2 diabetes.

Development and validation of a prediction model of perioperative hypoglycemia risk in patients with type 2 diabetes undergoing elective surgery

AimTo develop and validate a prediction model to evaluate the perioperative hypoglycemia risk in hospitalized type 2 diabetes mellitus (T2DM) patients undergoing elective surgery.MethodsWe retrospectively analyzed the electronic medical records of 1410 T2DM patients who had been hospitalized and undergone elective surgery. Regression analysis was used to develop a predictive model for perioperative hypoglycemia risk. The receiver operating characteristic (ROC) curve and the Hosmer–Lemeshow test were used to verify the model.ResultsOur study showed an incidence of 10.7% for level 1 hypoglycemia and 1.8% for level 2 severe hypoglycemia during the perioperative period. A perioperative hypoglycemic risk prediction model was developed that was mainly composed of four predictors: duration of diabetes ≥ 10 year, body mass index (BMI) < 18.5 kg/m2, standard deviation of blood glucose (SDBG) ≥ 3.0 mmol/L, and preoperative hypoglycemic regimen of insulin subcutaneous. Based on this model, patients were categorized into three groups: low, medium, and high risk. Internal validation of the prediction model showed high discrimination (ROC statistic = 0.715) and good calibration (no significant differences between predicted and observed risk: Pearson χ2 goodness-of-fit P = 0.765).ConclusionsThe perioperative hypoglycemic risk prediction model categorizes the risk of hypoglycemia using only four predictors and shows good reliability and validity. The model serves as a favorable tool for clinicians to predict hypoglycemic risk and guide future interventions to reduce hypoglycemia risk.

FC 125: Comparative Effectiveness of Dipeptidyl Peptidase-4 Inhibitors versus Sulfonylureas in Addition to Metformin: A Population-Based Cohort Study of Patients With Diabetes

Abstract BACKGROUND AND AIMS Effects of dipeptidyl peptidase-4 inhibitors (DPP4i) versus sulfonylureas (SU) in addition to metformin remain controversial in patients with type 2 diabetes. Some studies have shown that DPP4i provided cardioprotective effects, but some have reported that DPP4i showed neutral effects on cardiovascular outcomes. In addition, randomized clinical trials have reported inconsistent effects on heart failure among different types of DPP4i. This study aimed to clarify the conflicting cardiovascular effects of DPP4i and examine whether class effects exist among different types of DPP4i. METHOD We conducted a nationwide cohort study using claims data from the National Health Insurance in Taiwan from 2007 to 2013. We enrolled type 2 diabetes patients who received DPP4i or SU in addition to metformin. Participants who received DPP4i for at least three consecutive months were categorized as DPP4i users, and those who received SU for at least three consecutive months were categorized as SU users. DPP4i users were matched to SU users using propensity scores at a ratio of 1:1. The study outcomes were hospitalization for major adverse cardiovascular events (MACEs), heart failure, acute myocardial infarction, cerebrovascular disease, coronary revascularization and hypoglycemia. Univariate and multivariate Cox proportional hazards models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (95% CIs) for study outcomes comparing DPP4i with SU. RESULTS There were 37 317 matched pairs of DPP4i and SU users with a mean follow-up of 2.1 years. Compared with SU users, DPP4i users showed a significantly lower risk of hospitalization for MACE (HR 0.79, 95% CI 0.75–0.82), heart failure (HR 0.86, 95% CI 0.79–0.93), acute myocardial infarction (HR 0.79, 95% CI 0.68–0.92) and cerebrovascular disease (HR 0.72, 95% CI 0.67–0.77) (Table 1). Both sitagliptin and vildagliptin showed significantly lower risks of hospitalization for MACE (sitagliptin: adjusted HR 0.89, 95% CI 0.85–0.94; vildagliptin: adjusted HR 0.77, 95% CI 0.60–0.99) and cerebrovascular disease (sitagliptin: adjusted HR 0.81, 95% CI 0.75–0.88; vildagliptin: adjusted HR 0.64, 95% CI 0.44–0.92) (Figure 1). All three types of DPP4i users showed a significantly lower risk of hospitalization for hypoglycemia (sitagliptin: adjusted HR 0.45, 95% CI 0.39–0.52; vildagliptin: adjusted HR 0.22, 95% CI 0.09–0.52; saxagliptin: adjusted HR 0.38, 95% CI 0.17–0.83), but saxagliptin showed a borderline significantly higher risk of hospitalization for heart failure (HR 1.59, 95% CI 1.00–2.55). CONCLUSION DPP4i showed better protective effects than SU in outcomes of MACE, heart failure, acute myocardial infarction, cerebrovascular disease and hypoglycemia. Sitagliptin and vildagliptin showed more prominent protective effects. Compared with SU, all three types of DPP4i lowered the risk of hypoglycemia in patients with type 2 diabetes.

Hybrid Close-Loop Systems Versus Predictive Low-Glucose Suspend and Sensor-Augmented Pump Therapy in Patients With Type 1 Diabetes: A Single-Center Cohort Study.

IntroductionPredictive low-glucose suspend (PLGS) and hybrid closed-loop (HCL) systems may improve glucose control and quality of life in type 1 diabetic individuals. This is a cross-sectional, single-center study to compare the effect on metabolic control and glucose variability of PLGS and HCL systems as compared to standard sensor-augmented pump (SAP) therapy.MethodsWe retrospectively analyzed 136 adults (men/women 69/67, mean age 47.3 ± 13.9 years) with T1D on insulin pump therapy, divided accordingly to type of insulin pump system (group 1: SAP, 24 subjects; group 2: PLGS, 49 subjects; group 3: HCL, 63 subjects). The groups were matched for age, gender, years of disease, years of CSII use, and CGM wear time.ResultsThe analysis of CGM metrics, in the three groups, showed a statistically significant different percentage of time within the target range, defined as 70–180 mg/dl, with a higher percentage in group 3 and significantly less time spent in the hypoglycemic range in groups 2 and 3. The three groups were statistically different also for the glucose management indicator and coefficient of variation percentage, which were progressively lower moving from group 1 to group 3. In the HCL group, 52.4% of subjects reached a percentage of time passed in the euglycemic range above 70%, as compared to 32.7% in those with PLGS and 20.2% in those with SAP. A positive correlation between the higher percentage of TIR and the use of auto-mode was evident in the HCL group. Finally, the three groups did not show any statistical differences regarding the quality-of-life questionnaire, but there was a significant negative correlation between CV and perceived CSII-use convenience (r = -0.207, p = 0.043).ConclusionHCL systems were more effective in improving glucose control and in reducing the risk of hypoglycemia in patients with type 1 diabetes, thereby mitigating risk for acute and chronic complications and positively affecting diabetes technologies’ acceptance.