Comparison of glycemic control and variability in patients with type 2 and posttransplantation diabetes mellitus

Association of Muscle Mass Loss with Diabetes Development in Liver Transplantation Recipients

BACKGRUOUND

Post-transplant diabetes mellitus (PTDM) is one of the most significant complications after transplantation. Patients with end-stage liver diseases requiring transplantation are prone to sarcopenia, but the association between sarcopenia and PTDM remains to be elucidated. We aimed to investigate the effect of postoperative muscle mass loss on PTDM development.

METHODS

A total of 500 patients who underwent liver transplantation at a tertiary care hospital between 2005 and 2020 were included. Skeletal muscle area at the level of the L3-L5 vertebrae was measured using computed tomography scans performed before and 1 year after the transplantation. The associations between the change in the muscle area after the transplantation and the incidence of PTDM was investigated using a Cox proportional hazard model.

RESULTS

During the follow-up period (median, 4.9 years), PTDM occurred in 165 patients (33%). The muscle mass loss was greater in patients who developed PTDM than in those without PTDM. Muscle depletion significantly increased risk of developing PTDM after adjustment for other confounding factors (hazard ratio, 1.50; 95% confidence interval, 1.23 to 1.84; P=0.001). Of the 357 subjects who had muscle mass loss, 124 (34.7%) developed PTDM, whereas of the 143 patients in the muscle mass maintenance group, 41 (28.7%) developed PTDM. The cumulative incidence of PTDM was significantly higher in patients with muscle loss than in patients without muscle loss (P=0.034).

CONCLUSION

Muscle depletion after liver transplantation is associated with increased risk of PTDM development.

The Effect of Meal Glycemic Index and Meal Frequency on Glycemic Control and Variability in Female Nurses Working Night Shifts: A Two-Arm Randomized Cross-Over Trial

BACKGROUND

Night shift workers are exposed to circadian disruption, which contributes to impaired glucose tolerance. Although fasting during the night shift improves glucose homeostasis, adhering to this dietary strategy may be challenging.

OBJECTIVES

This study evaluated the effect of fasting compared with the consumption of meals with different combinations of glycemic index (GI, low or high) and frequency (1 or 3 times) during the night shift on continuous glucose monitoring metrics.

METHODS

A 2-arm randomized cross-over trial was conducted on female nurses working night shifts. In each of those arms, the participants were either provided with no meal (fasted), low GI, or high-GI meal during the night shift with a meal frequency according to which arm they were randomly allocated to, either 1-MEAL or 3-MEAL. Outcome variables were glycemic control and variability (GC and GV) metrics during the night shift (21:30-7:00), in the morning after the night shift (07:00-13:00), and in the 24 h period (18:00-18:00).

RESULTS

Compared to no meal, the consumption of 1 high-GI meal increased all GV metrics not only during the night shifts but also in the morning, for instance, as observed in the coefficient of variation (β = 0.03 mmol/L; 95% CI: 0.01, 0.05), and GV percentage (β = 4.13; 95% CI: 2.07, 6.18). The consumption of 1 or 3 low GI meals did not raise GC or GV metrics except for continuous overall net glycemic action during the night shifts after consuming 3 low GI meals. When controlling for GI, night shift meal frequency did not affect any metrics in any timeframe.

CONCLUSIONS

High meal GI but not higher meal frequency during the night shift increased GC and GV in female night shift workers. Results for 1 low-GI meal during the night shift were not different from a glucose profile after no meal. This trial was registered at trialsearch.who.int as NL8715.

Impact of total intravenous anesthesia and total inhalation anesthesia as the anesthesia maintenance approaches on blood glucose level and postoperative complications in patients with type 2 diabetes mellitus: a double-blind, randomized controlled trial

BACKGROUND

Diabetes mellitus is a prevalent metabolic disease in the world. Previous studies have shown that anesthetics can affect perioperative blood glucose levels which related to adverse clinical outcomes. Few studies have explored the choice of general anesthetic protocol on perioperative glucose metabolism in diabetes patients. We aimed to compare total intravenous anesthesia (TIVA) with total inhalation anesthesia (TIHA) on blood glucose level and complications in type 2 diabetic patients undergoing general surgery.

METHODS

In this double-blind controlled trial, 116 type 2 diabetic patients scheduled for general surgery were randomly assigned to either the TIVA group or TIHA group (n = 56 and n = 60, respectively). The blood glucose level at different time points were measured and analyzed by the repeated-measures analysis of variance. The serum insulin and cortisol levels were measured and analyzed with t-test. The incidence of complications was followed up and analyzed with chi-square test or Fisher's exact test as appropriate. The risk factors for complications were analyzed using the logistic stepwise regression.

RESULTS

The blood glucose levels were higher in TIHA group than that in TIVA group at the time points of extubation, 1 and 2 h after the operation, 1 and 2 days after the operation, and were significantly higher at 1 day after the operation (10.4 ± 2.8 vs. 8.1 ± 2.1 mmol/L; P < 0.01). The postoperative insulin level was higher in TIVA group than that in TIHA group (8.9 ± 2.9 vs. 7.6 ± 2.4 IU/mL; P = 0.011). The postoperative cortisol level was higher in TIHA group than that in TIVA group (15.3 ± 4.8 vs. 12.2 ± 8.9 ug/dL ; P = 0.031). No significant difference regarding the incidence of complications between the two groups was found based on the current samples. Blood glucose level on postoperative day 1 was a risk factor for postoperative complications (OR: 1.779, 95%CI: 1.009 ~ 3.138).

CONCLUSIONS

TIVA has less impact on perioperative blood glucose level and a better inhibition of cortisol release in type 2 diabetic patients compared to TIHA. A future large trial may be conducted to find the difference of complications between the two groups.

TRIAL REGISTRATION

The protocol registered on the Chinese Clinical Trials Registry on 20/01/2020 (ChiCTR2000029247).

Postoperative fasting plasma glucose and family history diabetes mellitus can predict post-transplantation diabetes mellitus in kidney transplant recipients

PURPOSE

To explore whether glycated albumin (GA) or fasting plasma glucose (FPG), both routinely monitored during patients' hospital stay, can be used to predict post-transplantation diabetes mellitus (PTDM).

METHODS

All kidney transplantation recipients (KTRs) from January 2017 to December 2018 were followed-up for 1 year. PTDM was diagnosed from day 45 post-operation to 1 year. When the completeness was above 80%, FPG or GA data on the day was selected, analyzed, and presented as range parameters and standard deviation (SD) and compared between PTDM and non-PTDM groups in fluctuation and stable periods. The predictive cut-off values were determined via receiver operating characteristic (ROC) analysis. The PTDM combined predictive mode, formed by the independent risk factors derived from logistic regression analyses, was compared with each independent risk factor with the independent ROC curve test.

RESULTS

Among 536 KTRs, 38 patients developed PTDM up to 1 year post-operatively. The family history diabetes mellitus (OR, 3.21; P = 0.035), the FPG SD in fluctuation period >2.09 mmol/L (OR, 3.06; P = 0.002), and the FPG maximum in stable period >5.08 mmol/L (OR, 6.85; P < 0.001) were the PTDM independent risk factors. The discrimination of the combined mode (area under the curve = 0.81, sensitivity = 73.68%, and specificity = 76.31%) was higher than each prediction (P < 0.05).

CONCLUSIONS

The FPG standard deviation during the fluctuation period, FPG maximum during the stable period, and family history diabetes mellitus predicted PTDM with good discrimination and potential routine clinical use.

Flash Glucose Monitoring to Assess Glycemic Control and Variability in Hemodialysis Patients: The GIOTTO Study

Background: Flash glucose monitoring (FGM) is a technology with considerable differences compared to continuous glucose monitoring (CGM), but it has been scarcely studied in hemodialysis patients. Thus, we aimed assessing the performance of FGM in such patients by comparison to self-monitoring of blood glucose (SMBG). We will also focus on estimation of glycemic control and variability, and their relationships with parameters of glucose homeostasis.

Methods: Thirty-one patients (20 with type 2 diabetes, T2DM, 11 diabetes-free, NODM) collected readings by FGM and SMBG for about 12 days on average. Readings by FGM and SMBG were compared by linear regression, Clarke error grid, and Bland-Altman analyses. Several indices of glycemic control and variability were computed. Ten patients also underwent oral glucose tolerance test (OGTT) for assessment of insulin sensitivity/resistance and insulin secretion/beta-cell function.

Results: Flash glucose monitoring and SMBG readings showed very good agreement in both T2DM and NODM (on average, 97 and 99% of readings during hemodialysis in A+B Clarke regions, respectively). Some glycemic control and variability indices were similar by FGM and SMBG (p = 0.06–0.9), whereas others were different (p = 0.0001–0.03). The majority of control and variability indices were higher in T2DM than in NODM, according to both FGM and SMBG (p = 0.0005–0.03). OGTT-based insulin secretion was inversely related to some variability indices according to FGM (R < −0.72, p < 0.02).

Conclusions: Based on our dataset, FGM appeared acceptable for glucose monitoring in hemodialysis patients, though partial disagreement with SMBG in glycemic control/variability assessment needs further investigations.

Association of British Clinical Diabetologists and Renal Association guidelines on the detection and management of diabetes post solid organ transplantation

Post-transplant diabetes mellitus (PTDM) is common after solid organ transplantation (SOT) and associated with increased morbidity and mortality for allograft recipients. Despite the significant burden of disease, there is a paucity of literature with regards to detection, prevention and management. Evidence from the general population with diabetes may not be translatable to the unique context of SOT. In light of emerging clinical evidence and novel anti-diabetic agents, there is an urgent need for updated guidance and recommendations in this high-risk cohort. The Association of British Clinical Diabetologists (ABCD) and Renal Association (RA) Diabetic Kidney Disease Clinical Speciality Group has undertaken a systematic review and critical appraisal of the available evidence. Areas of focus are; (1) epidemiology, (2) pathogenesis, (3) detection, (4) management, (5) modification of immunosuppression, (6) prevention, and (7) PTDM in the non-renal setting. Evidence-graded recommendations are provided for the detection, management and prevention of PTDM, with suggested areas for future research and potential audit standards. The guidelines are endorsed by Diabetes UK, the British Transplantation Society and the Royal College of Physicians of London. The full guidelines are available freely online for the diabetes, renal and transplantation community using the link below. The aim of this review article is to introduce an abridged version of this new clinical guideline ( https://abcd.care/sites/abcd.care/files/site_uploads/Resources/Position-Papers/ABCD-RA%20PTDM%20v14.pdf).

Current Pharmacological Intervention and Medical Management for Diabetic Kidney Transplant Recipients

Hyperglycemia after kidney transplantation is common in both diabetic and non-diabetic patients. Both pretransplant and post-transplant diabetes mellitus are associated with increased kidney allograft failure and mortality. Glucose management may be challenging for kidney transplant recipients. The pathophysiology and pattern of hyperglycemia in patients following kidney transplantation is different from those with type 2 diabetes mellitus. In patients with pre-existing and post-transplant diabetes mellitus, there is limited data on the management of hyperglycemia after kidney transplantation. The following article discusses the nomenclature and diagnosis of pre- and post-transplant diabetes mellitus, the impact of transplant-related hyperglycemia on patient and kidney allograft outcomes, risk factors and potential pathogenic mechanisms of hyperglycemia after kidney transplantation, glucose management before and after transplantation, and modalities for prevention of post-transplant diabetes mellitus.

Evolution of Glycemic Control and Variability After Kidney Transplant

BACKGROUND

The evolution of glycemic changes after kidney transplantation has not been described. We prospectively examined glycemic control and variability over time from transplantation using continuous glucose monitoring (CGM).

METHOD

Continuous glucose monitoring devices were fitted for 3 to 5 days at time of transplant, month 3, and month 6 posttransplant. Indices of glucose control (mean glucose, percent time in hyperglycemic range, and Glycemic Risk Assessment Diabetes Equation score) and variability were calculated. An oral glucose tolerance test was performed at month 3.

RESULTS

Twenty-eight patients (mean age, 45 ± 15 years) were enrolled, 64% male, 75% white, receiving tacrolimus, mycophenolate, and prednisolone (93%). Of 24 patients with complete CGM data at month 0, 3 had prior diabetes and 6 (25%) developed new-onset diabetes after transplant (NODAT). Hyperglycemia (>11.1 mM) was evident in 79% during days 0 to 3 posttransplant, particularly between 1 and 9 PM. Compared with recipients without diabetes, recipients with prior diabetes had higher mean glucose (7.8 mM; 95% confidence interval [CI], 7.4-8.2 vs 9.9 mM; 95% CI, 8.9-10.8; P < 0.001), Glycemic Risk Assessment Diabetes Equation (GRADE) score (4.5; 95% CI, 3.7-5.4 vs 7.8; 95% CI, 5.6-10.4; P = 0.003) and percent time with hyperglycemia. Glycemic control was also inferior in those that subsequently developed NODAT (mean glucose, 8.8 mM; 95% CI, 8.2-9.4; P = 0.004, GRADE: 6.2, 95% CI, 5.2-7.7; P = 0.04 vs no diabetes). Glucose variability was increased in patients with prior diabetes (glucose standard deviation, 1.99; 95% CI, 1.72-2.27 vs 2.97; 95% CI, 2.27-3.67; P = 0.006) but not in NODAT. All measures of glucose control and variability significantly improved over time after transplantation (P < 0.001).

CONCLUSIONS

Dysglycemia is very common after renal transplantation, exhibiting a distinct diurnal pattern of afternoon and evening hyperglycemia. The magnitude of hyperglycemia and variability are maximal in recipients with preexisting diabetes and significant in those who go on to develop NODAT. Dysglycemia improves with time.

The Degree of Hyperglycemia Excursion in Patients of Kidney Transplantation (KT) or Liver Transplantation (LT) Assessed by Continuous Glucose Monitoring (CGM): Pilot Study

OBJECTIVE

This study used a continuous glucose monitoring system (CGMS) to investigate the glucose profiles and assess the degree of hyperglycemic excursion after kidney or liver transplantation during the early period after operation.

METHODS

Patients to whom a CGMS was attached during a postoperative period of approximately one month after transplantation were included. The CGM data of 31 patients including 24 with kidney transplantation (KT) and seven with liver transplantation (LT) were analyzed.

RESULTS

Hyperglycemia over 126 mg/dL (fasting) or 200 g/dL (postprandial) occurred in 42.1% (8/19) and 16.7% (1/6) of KT and LT patients, respectively, during this early period after transplantation, except for patients with preexisting diabetes (5 KT, 1 LT). The average mean amplitude of glycemic excursion (MAGE) and mean absolute glucose (MAG) levels were 91.18 ± 26.51 vs. 65.66 ± 22.55 (P < 0.05) and 24.62 ± 7.78 vs. 18.18 ± 7.07 (P < 0.05) in KT vs. LT patients, respectively, in patients without preexisting DM or PTDM patients who showed normal glucose levels. Average increase from the lowest level to the peak glucose value was higher in KT patients than LT patients (P < 0.05). Conclusions. The transplanted organ also needs to be considered as an important factor affecting glucose control and the occurrence of more severe glucose excursions in patients who receive transplantation although immunosuppression agents are well-known important factors; however, our study was limited to the early posttransplantation period. Further studies involving CGM follow-up at regular intervals based on the time since transplantation are needed.

A randomized controlled trial-based algorithm for insulin-pump therapy in hyperglycemic patients early after kidney transplantation

Treating hyperglycemia in previously non-diabetic individuals with exogenous insulin immediately after kidney transplantation reduced the odds of developing Posttransplantation Diabetes Mellitus (PTDM) in our previous proof-of-concept clinical trial. We hypothesized that insulin-pump therapy with maximal insulin dosage during the afternoon would improve glycemic control compared to basal insulin and standard-of-care. In a multi-center, randomized, controlled trial testing insulin isophane for PTDM prevention, we added a third study arm applying continuous subcutaneous insulin lispro infusion (CSII) treatment. CSII was initiated in 24 patients aged 55±12 years, without diabetes history, receiving tacrolimus. The mean daily insulin lispro dose was 9.2±5.2 IU. 2.3±1.1% of the total insulin dose were administered between 00:00 and 6:00, 19.5±11.6% between 6:00 and 12:00, 62.3±15.6% between 12:00 and 18:00 and 15.9±9.1% between 18:00 and 24:00. Additional bolus injections were necessary in five patients. Mild hypoglycemia (52–60 mg/dL) occurred in two patients. During the first post-operative week glucose control in CSII patients was overall superior compared to standard-of-care as well as once-daily insulin isophane for fasting and post-supper glucose. We present an algorithm for CSII treatment in kidney transplant recipients, demonstrating similar safety and superior short-term efficacy compared to standard-of-care and once-daily insulin isophane.

Effect of Oral Pre-Meal Administration of Betaglucans on Glycaemic Control and Variability in Subjects with Type 1 Diabetes

We conducted a double-blind placebo-controlled crossover pilot study to investigate the effect of oat betaglucans (β-glucan) on glycaemic control and variability in adults with type 1 diabetes (T1D; n = 14). Stomacol^® tablets (1.53 g of β-glucan) or placebo (Plac) were administered three times daily before meals for two weeks. Glucose levels were monitored during the second week by continuous glucose monitoring (CGM). There was an increase in basic measures of glycaemic control (maximal glucose value 341 ± 15 vs. 378 ± 13 mg/dL for Plac and β-glucan, p = 0.004), and average daily risk range (62 ± 5 vs. 79 ± 4 mg/dL for Plac and β-glucan, p = 0.003) favouring Plac over β-glucan, but no increase in the M-value (the weighted average of the glucose values) or other more complex measures. Basic measures of glucose variability were also slightly increased during β-glucan treatment, with no difference in more complex measures. However, glycaemic variability increased between the first and last two CGM days on Plac, but remained unchanged on β-glucan. In conclusion, in this pilot study we were unable to demonstrate a general positive effect of β-glucan before meals on glucose control or variability in T1D.

Insulin resistance and beta-cell dysfunction in people with prediabetes according to criteria based on glycemia and glycosylated hemoglobin

Impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) are well-known conditions of risk for diabetes. Recently, 1h-hyperglycemia (1h-HG), i.e., glycemia > 8.6 mmol/L, has been suggested as further condition of diabetes risk. Moreover, in 2010 the American Diabetes Association included the measurement of glycosylated hemoglobin (HbA1c) among the criteria of diabetes risk (5.7-6.4%). Aim of this study was investigating all these different conditions of diabetes risk, with specific focus on possible insulin sensitivity and beta-cell function changes, when 1h-HG, and further HbA1c-prediabetes, are added to the already deeply studied condition of IFG/IGT. In this study, we retrospectively analysed 744 participants that underwent 2h-OGTT and HbA1c measurement. Participants were stratified into groups: (i) normal glucose tolerance, NGT (n=178); (ii) IFG and/or IGT (n=88); (iii) IFG/IGT plus 1h-HG (n=342); (iv) IFG/IGT plus 1h-HG plus HbA1c-prediabetes (n=136). We calculated several indices of insulin sensitivity and beta-cell function, as well as an index considering both aspects (disposition index). We found that progressing from group (i) to group (iv) both insulin sensitivity and beta-cell function tended to further deteriorate; the parameter providing more evidence was the disposition index (p<0.008 in any group comparison). In conclusion, for appropriate assessment of the level of diabetes risk (especially in people already known to be at high risk), it may be convenient to measure all the indicated parameters, that is, glycemia at fasting, at one hour and two hours during OGTT, and glycosylated hemoglobin.