Evidence suggests that the Endothelial Activation and Stress Index (EASIX) predicts mortality in endothelium-related conditions, but its link to mortality risk in diabetes remains unclear. This study investigates the association between EASIX and mortality risk in diabetes patients.
We included 3,252 diabetes patients from seven National Health and Nutrition Examination Survey cycles (2003–2016). Mortality data were sourced from National Death Index records. Restricted cubic spline (RCS) regression assessed the EASIX-mortality risk relationship, while the maximally selected rank statistics method (MSRSM) identified the optimal EASIX cutoff for survival outcomes. Weighted multivariable Cox regression models evaluated the association of EASIX with all-cause and cardiovascular mortality.
Over a median follow-up of 91 months, 895 (27.5%) of 3,252 diabetes patients died, including 260 (8.0%) from cardiovascular and 635 (19.5%) from noncardiovascular causes. RCS analysis showed a positive association between EASIX and both all-cause and cardiovascular mortality. Each one-unit EASIX increase raised all-cause and cardiovascular mortality risks by 27% and 24%, respectively. MSRSM classified patients into higher (>0.70) and lower (≤0.70) EASIX groups. Those with higher EASIX had a significantly greater risk of all-cause (HR 1.56, 95% CI 1.21–2.01) and cardiovascular mortality (HR 2.05, 95% CI 1.33–3.16). Time-dependent receiver operating characteristic analysis showed AUCs for 1-, 3-, 5-, and 10-year survival were 0.78, 0.72, 0.70, and 0.69 (all-cause) and 0.90, 0.81, 0.76, and 0.73 (cardiovascular).
Elevated EASIX is independently associated with increased all-cause and cardiovascular mortality in diabetes patients, highlighting its potential as a valuable clinical biomarker.
Hyperglycemic emergencies are broadly classified into diabetic ketoacidosis and hyperosmotic hyperglycemic state. The purpose of this study was to develop a clinical model for predicting treatment of hyperglycemic emergencies.
This study is a multicenter, retrospective cohort study. We used information on patients admitted to four medical institutions for treatment for hyperglycemic emergencies by diabetologists between April 1, 2010, and March 31, 2024, as the machine learning's training data. Multiple regression analysis was performed to find parameters that correlated with the difference between blood glucose levels before and after treatment initiation (ΔGlu), and a gradient boosting decision tree (GBDT) was created to predict ΔGlu.
Patients with type 1 diabetes (n = 47) and type 2 diabetes (n = 116) were included in the analysis of this study. We created a GBDT model using the following parameters as features: blood glucose level at the start of continuous intravenous insulin therapy, bicarbonate concentration, insulin flow rate, time elapsed since the start of continuous insulin therapy, and drip flow, which are important parameters for continuous intravenous insulin therapy for hyperglycemic emergencies. As a result, the correlation coefficient between predicted ΔGlu and actual ΔGlu was 0.83, showing a strong positive correlation.
A GBDT model was developed to predict treatment after continuous intravenous insulin therapy using several variables during emergency care of patients with hyperglycemic emergencies. It is hoped that the application of this GBDT will allow appropriate provision of initial treatment, especially in nonspecialized medical facilities.
Maternal hyperglycemia is associated with heavy for date (HFD) infants. Considering the association between body composition and hyperglycemia, we investigated the changes in maternal body composition and their relationship with HFD infants in pregnant women with diabetes.
Body composition was measured during pregnancy using a bioelectrical impedance analysis system. This retrospective study included 151 pregnant women; 27 women had type 1 diabetes mellitus (DM), 21 had type 2 DM, 101 were diagnosed with gestational DM, and 2 had overt DM. The number of HFD infants was 40.
In the non-type 1 DM group, change in fat mass (ΔFM) (P < 0.01) and pre-pregnancy BMI (P < 0.05) were risk factors for HFD. In the insulin group, ΔFM, pre-pregnancy BMI, and age (all P < 0.05) were risk factors for HFD. The area under the curve was 0.813 for the predictive model combined with ΔFM and pre-pregnancy BMI in the non-type 1 DM group and 0.818 for the model combined with ΔFM, pre-pregnancy BMI, and age in the insulin group.
The combination of body composition parameters and clinical data may predict HFD in pregnant women with diabetes.
HbA1c and body weight were assessed across selected subgroups of adults with type 2 diabetes receiving oral semaglutide in clinical practice.
In this non-interventional study, changes in HbA1c and body weight to end of study (EoS) and safety were assessed by subgroup: baseline age, body mass index (BMI), type 2 diabetes duration, participants switching from dipeptidyl peptidase-4 inhibitors, and semaglutide dose at EoS.
All subgroups experienced reductions in HbA1c and body weight. Younger participants had greater reductions in HbA1c than older participants (−0.9, −0.7, −0.7, and −0.5 percentage points for participants aged <55, ≥55–<65, ≥65–<75, and ≥75 years, respectively [P = 0.0467]). Shorter type 2 diabetes duration and lower EoS semaglutide dose were associated with greater HbA1c reductions (−0.8, −0.7, and −0.6 percentage points with ≤5, >5–≤10, and >10 years' duration, respectively [P < 0.0001]; −1.2, −0.7, and −0.4 percentage points with 3, 7, and 14 mg, respectively [P < 0.0001]). Changes in HbA1c were not significantly different across other subgroups. Lower EoS semaglutide dose was associated with greater body weight reductions (−3.8, −2.9, and −2.8 kg with 3, 7, and 14 mg, respectively [P < 0.0001]); body weight reductions were not significantly different across other subgroups. Adverse events were similar between subgroups, except that older subgroups experienced more events.
HbA1c and body weight decreased across all subgroups, providing insights into oral semaglutide use in clinical practice for individuals with different characteristics in the real-world setting.
Dear Editor,
We read with great interest the article ‘A prediction model for diabetes complications using the Kokuho Database and its application to public health services in Japan’1. This study represents a valuable endeavor in developing a diabetes complication prediction model leveraging large-scale data, with substantial implications for public health practice in Japan. However, two methodological considerations warrant further clarification to enhance the scientific rigor and clinical translatability of the findings.
First, the selection of a 6-year historical stratification window for ischemic heart disease and cerebrovascular disease requires mechanistic and empirical justification. Epidemiological evidence consistently indicates that diabetic macrovascular complications exhibit stronger associations with short-term metabolic control parameters (1–3 years) such as glycemic variability and blood pressure trajectories, rather than distant historical data2, 3. A 6-year window may introduce collinearity by over-including less relevant historical records, potentially attenuating the weight of dynamic, time-sensitive risk factors. In addition, long-term indicators may not directly reflect the current risk status of the patient. The overall risk factor status of the patient during this period may change due to various factors (such as improved lifestyle, changes in medication, and the occurrence of other comorbidities). We urge the authors to clarify whether systematic comparisons of alternative time frames (1, 3, and 6 years) were performed to validate the optimal stratification criteria for predictive accuracy.
Second, the comparative prognostic value of early lifestyle determinants vs late biochemical markers in the model merits discussion. Lifestyle factors (sedentary behavior, dietary patterns, and smoking) typically precede biochemical abnormalities by 5–10 years, offering a critical window for primordial prevention. From a translational perspective, these modifiable behaviors represent more actionable targets for public health interventions compared to established metabolic derangements. Did the authors perform subgroup analyses to quantify the relative predictive power of these variables, particularly regarding their incremental value in early risk stratification?
Addressing these points would significantly strengthen the study's methodological robustness and public health relevance. We commend the authors for their contribution to this important field and anticipate their clarifications.
The authors declare no conflict of interest.
Approval of the research protocol: N/A.
Informed consent: N/A.
Registry and the registration no. of the study/trial: N/A.
Animal studies: N/A.
None.
Yanna Le and Qingyun Xu: methodology, writing—review and editing. Feiqi Xu: methodology, writing.
Erythropoietin-producing hepatoma A2 (EphA2), a receptor for progranulin (PGRN), which is a growth factor associated with metabolic disorders, is implicated in inflammation and atherosclerotic diseases. Since both EphA2 and PGRN play roles in diabetic kidney disease (DKD) and cardiovascular disease (CVD), this study examined their association with renal function and CVD markers in individuals with diabetes. In addition, the diagnostic value of EphA2 and PGRN in predicting renal impairment was evaluated.
Circulating EphA2 and PGRN levels were measured using enzyme-linked immunosorbent assay in 735 participants with diabetes. Clinical data, including biometric parameters, physiological measurements, and comorbidities, were collected for analysis.
Both EphA2 and PGRN levels positively correlated with older age, elevated blood pressure, higher urinary albumin-to-creatinine ratio (UACR), and brain natriuretic peptide (BNP) levels but negatively correlated with estimated glomerular filtration rate (eGFR). Multivariate logistic regression analysis revealed that EphA2 was an independent predictor of eGFR <60 mL/min/1.73 m2, even after adjusting for UACR and CVD risk factors and glycated hemoglobin. Although PGRN was also independently associated with eGFR <60 mL/min/1.73 m2, its association was weaker than that of EphA2. Conversely, when UACR ≥30 mg/g was used as the dependent variable, PGRN emerged as a stronger independent determinant than EphA2, even after adjusting for eGFR and CVD risk factors.
EphA2 and PGRN levels are significantly associated with renal function in individuals with diabetes. These findings suggest that EphA2 and PGRN could serve as novel biomarkers for kidney impairment, independent of established CVD markers in this population.
This study assesses the accuracy of an ultra-low-cost insulin pump for regulating blood glucose, an essential component of the artificial pancreas.
The primary objective is to assess the accuracy of blood glucose regulation using an ultra-low-cost insulin pump compared with insulin injections.
The study was conducted using 14 male Wistar rats aged 14 weeks, which were induced with diabetes through a streptozotocin procedure. The control group (seven subjects) was treated with subcutaneous insulin injections, and the study group (seven subjects) was treated with the experimental insulin pump.
Our findings reveal that the ultra-low-cost insulin pump performs comparably to insulin injections for glycemia regulation using standard clinical insulin treatment. The blood glucose records obtained by CGM indicate that trends in both methods correspond, with a glycemia median absolute relative difference of 6.1748 mg/dL and an interquartile range of 14.57 mg/dL. The response time to reach glucose levels between 70 and 180 mg/dL was around 55 min, testing different amounts of insulin units ranging from 0.09 to 2.06 U. Also, four obstruction cases were detected by the system in an average time of 3 s, showing compliance with the EN60601-2-24 standard.
The in vivo experiments with diabetic rats and ultra-low-cost insulin pumps demonstrate that these prototypes can be used for glucose regulation in clinical trials while complying with the conditions of the EN60601-2-24 standard. Future work should focus on conducting insulin pump tests in a closed loop and a cybersecurity study to prevent any possible external attack.
Diabetes, a growing global public health issue, is often associated with autoimmune liver diseases (AILD), such as autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and primary sclerosing cholangitis (PSC), in both type 1 diabetes (T1DM) and type 2 diabetes (T2DM). However, the causal relationship between these conditions remains unclear.
We used two-sample bidirectional Mendelian randomization (MR) to explore causal links between AILD and diabetes. Genetic variants identified from genome-wide association studies were used as instruments. Sensitivity and multivariable analyses adjusted for potential confounders were performed to assess the robustness of findings.
Genetically predicted PBC (OR 1.41, 95% CI 1.19–1.67) and PSC (OR 1.39, 95% CI 1.14–1.68) were associated with an increased risk of T1DM. PBC (OR 1.03, 95% CI 1.01–1.05) was also linked to a higher risk of T2DM. In reverse MR analysis, genetically predicted T1DM was associated with an increased risk of PBC (OR 1.28, 95% CI 1.09–1.50), PSC (OR 1.27, 95% CI 1.14–1.41), and AIH (OR 1.13, 95% CI 1.06–1.19). Multivariable MR confirmed the causal effect of PBC and PSC on T1DM, but the link between PBC and T2DM weakened after adjusting for BMI and inflammatory bowel disease.
This study identifies bidirectional causal relationships between PBC/PSC and T1DM, suggesting shared pathogenesis, and T1DM also raises the risk for AIH. Although the link between PBC and T2DM weakened after adjusting for confounders, it highlights the complexity of genetic and environmental interactions, underscoring the importance of diabetes screening in AILD patients and guiding potential targeted therapies.
Achieving optimal blood pressure control remains challenging, particularly for patients with diabetes. This post-hoc study compared the efficacy and safety of switching to sacubitril/valsartan vs adding thiazide diuretics in this population.
This retrospective study included patients with diabetes and inadequate blood pressure control in the office or home settings despite combination therapy with renin-angiotensin system inhibitors and calcium channel blockers. Patients were categorized into those receiving an additional thiazide diuretic (THZ group, n = 136) and those switching to sacubitril/valsartan (SacVal group, n = 199). The treatment effects over 12 months were analyzed using propensity score analysis with inverse probability weighting. Treatment discontinuation rates were assessed using the Cox proportional hazards model.
In the propensity score analysis model, target pressure achievement rates were similar between the two groups. However, compared to the THZ group, the SacVal group exhibited significantly lower uric acid levels (P < 0.001), improved glycated hemoglobin A1c (P = 0.02), and a smaller estimated glomerular filtration rate decline (P = 0.02). Treatment discontinuation due to adverse events was significantly higher in the THZ group (13% vs 1%), with a hazard ratio of 11.53 (95% confidence interval: 2.66–49.93, P < 0.001).
The combination of sacubitril/valsartan with calcium channel blockers provided a similar reduction in blood pressure compared with thiazide with renin-angiotensin system inhibitors and calcium channel blockers, and reported more favorable changes in uric acid levels, glycated hemoglobin A1c, and estimated glomerular filtration rate, along with a significantly better treatment tolerability.
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