Objective To evaluate the effects of glucose-containing dialysate versus glucose-free dialysate on blood pressure variability and blood glucose variability in maintenance hemodialysis (MHD) patients and to assess safety. Methods MHD patients from 12 hospitals were enrolled between October 2024 and June 2025. According to the randomized block design, patients were randomly divided into the glucose-containing dialysate group (experimental group) and the glucose-free dialysate group (control group). During hemodialysis sessions, blood pressure were monitored at 0, 1, 2, 3, and 4 hours, and blood glucose was measured at 0, 2, and 4 hours monthly for six consecutive months. Hypotension episodes and hypoglycemic episodes were recorded throughout dialysis. Results A total of 244 MHD patients were included, with 122 in each group. Compared with the control group, the experimental group showed significantly lower systolic blood pressure variability [dialysis for 2 hours: 9.92 (7.92, 12.52) vs. 11.95 (9.45, 15.36) mm Hg (1 mm Hg=0.133 kPa), P<0.001; during the 0-2 hour dialysis period: 2.60 (1.24, 3.97) vs. 3.74 (2.03, 6.52) mm Hg, P=0.011], diastolic blood pressure variability [during the 0-4 hour dialysis period: 3.85 (1.49, 6.69) vs. 4.72 (1.99, 8.46) mm Hg, P<0.001], blood glucose variability [dialysis for 2 hours: 0.16 (0.12, 0.20) vs. 0.18 (0.13, 0.23) mmol/L, P=0.002; dialysis for 4 hours: 0.17 (0.13, 0.22) vs. 0.21 (0.17, 0.26) mmol/L, P<0.001; during the 2-4 hour dialysis period: 0.04 (0.02, 0.08) vs. 0.07 (0.03, 0.10) mmol/L, P=0.004], incidence rates of hypotension (32.9% vs. 33.3%, P=0.005) and incidence rates of hypoglycemia (0.42% vs. 4.02%, P<0.001). Conclusions Glucose-containing dialysate reduces both blood pressure variability and blood glucose variability more effectively than glucose-free dialysate during hemodialysis. Compared with glucose-free dialysate, the glucose-containing dialysate demonstrated a lower incidence of hypotension episodes and hypoglycemic episodes.
Objective To explore the predictive value of blood glucose variability indices [coefficient of variation (CV) of blood glucose, mean amplitude of glycemic excursions (MAGE), and largest amplitude of glycemic excursions (LAGE)] for 6-month readmission in hospitalized patients with chronic obstructive pulmonary disease (COPD) complicated with respiratory failure, and analyze the dose-effect relationship. Meanwhile, to compare the predictive efficacy of blood glucose variability indices between subgroups with and without type 2 diabetes. Methods The clinical data of 102 inpatients with COPD complicated with respiratory failure admitted to Zhangjiagang Hospital Affiliated to Soochow University from January 2022 to March 2025 were retrospectively collected. According to whether they were readmitted within 6 months, they were divided into a readmission group (32 cases) and a non-readmission group (70 cases). At the same time, they were divided into a type 2 diabetes subgroup (41 cases) and a non-type 2 diabetes subgroup (61 cases) based on whether they had type 2 diabetes. The independent risk factors for 6-month readmission were analyzed by the multivariate Cox proportional hazards regression model, and the regression models for the total population and different diabetes subgroups were constructed respectively. The predictive efficacy of blood glucose variability indices was evaluated by the receiver operating characteristic (ROC) curve, and the predictive value of blood glucose variability indices in different subgroups was compared. The dose-effect relationship between blood glucose variability index and readmission risk was analyzed by the restricted cubic spline (RCS) model, and the dose-effect characteristics of different diabetes subgroups were explored. Results The multivariate Cox regression model showed that MAGE, CV, LAGE, and APACHEⅡ score were independent risk factors for 6-month readmission, while albumin was an independent protective factor (P<0.05). In the type 2 diabetes subgroup, MAGE and APACHEⅡ score were independent risk factors for 6-month readmission (P<0.05); in the non-type 2 diabetes subgroup, MAGE, CV, and albumin were independent influencing factors for 6-month readmission (P<0.05). ROC curve analysis showed that in the total population, the areas under curve (AUCs) of CV, MAGE, and LAGE for predicting 6-month readmission were 0.789, 0.809, and 0.753, respectively, all of which were lower than the AUC of the combined detection (0.891). In the type 2 diabetes subgroup, the AUC of MAGE for predicting readmission was 0.825, and in the non-type 2 diabetes subgroup, the AUC of MAGE for predicting readmission was 0.796, both of which were the most effective blood glucose variability indicators in their respective subgroups. The RCS model combined with Cox regression analysis showed that in the total population, MAGE was linearly and positively correlated with the 6-month readmission risk of patients (non-linear test P=0.326), with no obvious threshold effect: when MAGE≤5.83 mmol/L, the trend of readmission risk increase was relatively gentle; when MAGE>5.83 mmol/L, the readmission risk increased at an accelerating rate, and for every 1 mmol/L increase in MAGE, the 6-month readmission risk of patients significantly increased by 18.6%. In the type 2 diabetes subgroup, MAGE was linearly and positively correlated with the readmission risk (non-linear test P=0.289), and for every 1 mmol/L increase in MAGE, the readmission risk increased by 21.5%. In the subgroup without type 2 diabetes, MAGE was also linearly and positively correlated with the risk of readmission (non-linear test P=0.365), and for every 1 mmol/L increase in MAGE, the risk of readmission increased by 16.3%. Conclusions The glycemic variability indices (especially MAGE) have high predictive value for 6-month readmission of hospitalized patients with COPD and respiratory failure. Moreover. MAGE shows a linear dose-effect relationship with the risk of readmission. This predictive value holds true in both the type 2 diabetes and non-type 2 diabetes subgroups. MAGE is the optimal predictive indicator in both subgroups. Clinically, controlling blood glucose fluctuations can reduce the risk of readmission, and individualized blood glucose fluctuation control targets can be set for patients with different diabetes statuses.