Glycemic Control Reduces Infections in Post-Liver Transplant Patients: Results of a Prospective, Randomized Study

Factors associated with the time required for CRP normalization in pyogenic spondylitis: A retrospective observational study

Background

Treatment for pyogenic spondylitis tends to be prolonged; however, few studies have examined the factors associated with the time required for infection control. Therefore, we analyzed a consecutive cohort of patients to identify factors associated with the time required to control infection in pyogenic spondylitis. This study aimed to clarify the factors linked to the duration necessary for achieving infection control in cases of pyogenic spondylitis, using C-reactive protein (CRP) normalization as an indicator.

Methods

In this retrospective observational study, we investigated 108 patients diagnosed with pyogenic spondylitis. We evaluated the number of days from the first visit to CRP normalization; for cases wherein CRP did not normalize, the number of days to the date of final blood sampling was evaluated. In the present study, infection control in pyogenic spondylitis was defined as a CRP falling within the normal range (≤0.14 mg/dL). We performed univariate and multivariate Cox regression analyses to identify various factors associated with the time required for CRP normalization in pyogenic spondylitis.

Results

The mean time required for CRP normalization was 148 days. Univariate Cox regression analysis showed that the serum creatinine level, estimated glomerular filtration rate (eGFR), lymphocyte percentage, neutrophil percentage, CRP level, CRP-albumin ratio, and neutrophil-to-lymphocyte ratio were significantly associated with the time required to control infection. Multivariate Cox regression analysis showed that a higher neutrophil percentage, diabetes mellitus, and a lower eGFR were the independent factors associated with a longer infection control time.

Conclusions

We found that a higher neutrophil percentage, diabetes mellitus, and a lower eGFR were significantly associated with a longer time for CRP normalization in pyogenic spondylitis. These findings may help identify patients with pyogenic spondylitis who are at a high risk for an extended infection control period.

Early post-transplant hyperglycemia and post-transplant diabetes mellitus following heart transplantation

INTRODUCTION

Heart transplantation is an important treatment for end-stage heart failure. Early post-transplant hyperglycemia (EPTH) and post-transplant diabetes mellitus (PTDM) are common following heart transplantation and are associated with increased morbidity and mortality.

AREAS COVERED

This review summarizes the clinical characteristics, diagnosis, and treatment of EPTH and PTDM in cardiac transplant patients, incorporating findings from non-cardiac solid organ transplant studies where relevant due to limited heart-specific research.

EXPERT OPINION

EPTH following heart transplantation is common yet understudied and is associated with the later development of PTDM. PTDM is associated with adverse outcomes including infection, renal dysfunction, microvascular disease, and an increased risk of re-transplantation and mortality. Risk factors for EPTH include the post-operative immunosuppression regimen, recipient and donor age, body mass index, infections, and chronic inflammation. Early insulin treatment is recommended for EPTH, whereas PTDM management is varied and includes lifestyle modification, anti-glycemic agents, and insulin. Given the emerging evidence on the transplant benefits associated with effective glucose control, and the cardioprotective potential of newer anti-glycemic agents, further focus on the management of EPTH and PTDM within heart transplant recipients is imperative.

Perioperative glycaemic control for people with diabetes undergoing surgery

BACKGROUND

People with diabetes mellitus are at increased risk of postoperative complications. Data from randomised clinical trials and meta-analyses point to a potential benefit of intensive glycaemic control, targeting near-normal blood glucose, in people with hyperglycaemia (with and without diabetes mellitus) being submitted for surgical procedures. However, there is limited evidence concerning this question in people with diabetes mellitus undergoing surgery.

OBJECTIVES

To assess the effects of perioperative glycaemic control for people with diabetes undergoing surgery.

SEARCH METHODS

For this update, we searched the databases CENTRAL, MEDLINE, LILACS, WHO ICTRP and ClinicalTrials.gov. The date of last search for all databases was 25 July 2022. We applied no language restrictions.

SELECTION CRITERIA

We included randomised controlled clinical trials (RCTs) that prespecified different targets of perioperative glycaemic control for participants with diabetes (intensive versus conventional or standard care).

DATA COLLECTION AND ANALYSIS

Two authors independently extracted data and assessed the risk of bias. Our primary outcomes were all-cause mortality, hypoglycaemic events and infectious complications. Secondary outcomes were cardiovascular events, renal failure, length of hospital and intensive care unit (ICU) stay, health-related quality of life, socioeconomic effects, weight gain and mean blood glucose during the intervention. We summarised studies using meta-analysis with a random-effects model and calculated the risk ratio (RR) for dichotomous outcomes and the mean difference (MD) for continuous outcomes, using a 95% confidence interval (CI), or summarised outcomes with descriptive methods. We used the GRADE approach to evaluate the certainty of the evidence (CoE).

MAIN RESULTS

A total of eight additional studies were added to the 12 included studies in the previous review leading to 20 RCTs included in this update. A total of 2670 participants were randomised, of which 1320 were allocated to the intensive treatment group and 1350 to the comparison group. The duration of the intervention varied from during surgery to five days postoperative. No included trial had an overall low risk of bias. Intensive glycaemic control resulted in little or no difference in all-cause mortality compared to conventional glycaemic control (130/1263 (10.3%) and 117/1288 (9.1%) events, RR 1.08, 95% CI 0.88 to 1.33; I2 = 0%; 2551 participants, 18 studies; high CoE). Hypoglycaemic events, both severe and non-severe, were mainly experienced in the intensive glycaemic control group. Intensive glycaemic control may slightly increase hypoglycaemic events compared to conventional glycaemic control (141/1184 (11.9%) and 41/1226 (3.3%) events, RR 3.36, 95% CI 1.69 to 6.67; I2 = 64%; 2410 participants, 17 studies; low CoE), as well as those considered severe events (37/927 (4.0%) and 6/969 (0.6%), RR 4.73, 95% CI 2.12 to 10.55; I2 = 0%; 1896 participants, 11 studies; low CoE). Intensive glycaemic control, compared to conventional glycaemic control, may result in little to no difference in the rate of infectious complications (160/1228 (13.0%) versus 224/1225 (18.2%) events, RR 0.75, 95% CI 0.55 to 1.04; P = 0.09; I2 = 55%; 2453 participants, 18 studies; low CoE). Analysis of the predefined secondary outcomes revealed that intensive glycaemic control may result in a decrease in cardiovascular events compared to conventional glycaemic control (107/955 (11.2%) versus 125/978 (12.7%) events, RR 0.73, 95% CI 0.55 to 0.97; P = 0.03; I2 = 44%; 1454 participants, 12 studies; low CoE). Further, intensive glycaemic control resulted in little or no difference in renal failure events compared to conventional glycaemic control (137/1029 (13.3%) and 158/1057 (14.9%), RR 0.92, 95% CI 0.69 to 1.22; P = 0.56; I2 = 38%; 2086 participants, 14 studies; low CoE). We found little to no difference between intensive glycaemic control and conventional glycaemic control in length of ICU stay (MD -0.10 days, 95% CI -0.57 to 0.38; P = 0.69; I2 = 69%; 1687 participants, 11 studies; low CoE), and length of hospital stay (MD -0.79 days, 95% CI -1.79 to 0.21; P = 0.12; I2 = 77%; 1520 participants, 12 studies; very low CoE). Due to the differences within included studies, we did not pool data for the reduction of mean blood glucose. Intensive glycaemic control resulted in a mean lowering of blood glucose, ranging from 13.42 mg/dL to 91.30 mg/dL. One trial assessed health-related quality of life in 12/37 participants in the intensive glycaemic control group, and 13/44 participants in the conventional glycaemic control group; no important difference was shown in the measured physical health composite score of the short-form 12-item health survey (SF-12). One substudy reported a cost analysis of the population of an included study showing a higher total hospital cost in the conventional glycaemic control group, USD 42,052 (32,858 to 56,421) compared to the intensive glycaemic control group, USD 40,884 (31.216 to 49,992). It is important to point out that there is relevant heterogeneity between studies for several outcomes. We identified two ongoing trials. The results of these studies could add new information in future updates on this topic.

AUTHORS' CONCLUSIONS

High-certainty evidence indicates that perioperative intensive glycaemic control in people with diabetes undergoing surgery does not reduce all-cause mortality compared to conventional glycaemic control. There is low-certainty evidence that intensive glycaemic control may reduce the risk of cardiovascular events, but cause little to no difference to the risk of infectious complications after the intervention, while it may increase the risk of hypoglycaemia. There are no clear differences between the groups for the other outcomes. There are uncertainties among the intensive and conventional groups regarding the optimal glycaemic algorithm and target blood glucose concentrations. In addition, we found poor data on health-related quality of life, socio-economic effects and weight gain. It is also relevant to underline the heterogeneity among studies regarding clinical outcomes and methodological approaches. More studies are needed that consider these factors and provide a higher quality of evidence, especially for outcomes such as hypoglycaemia and infectious complications.

Predictors of Mortality During Initial Liver Transplant Hospitalization and Investigation of Causes of Death

Background

Liver transplant (LT) remains a life-saving procedure with a high mortality rate. The present study investigated the causes of death and sought to identify predictive factors of mortality during the initial LT hospitalization.

Material/Methods

We retrieved data on first-time adult recipients who underwent LT between November 2017 and October 2019 receiving grafts from donation after citizen’s death. The risk factors for mortality during the initial LT hospitalization were confirmed by univariate analysis. We also analyzed the causes of death.

Results

We enrolled 103 recipients, including 86 males and 17 females, with a mean age of 47.7 years. Thirty-eight (36.9%) recipients were labeled as non-cholestatic cirrhosis-related indications. Approximately 8% of all recipients had diabetes prior to LT. Induction therapy was used in 11 (10.7%) recipients, along with maintenance therapy. The median model for end-stage liver disease score at LT was 32.4 (21.4–38.4). The in-hospital mortality rate of LT recipients was 6.8% (7/103), and infections were responsible for most of the deaths (6/7). The 1 remaining death resulted from primary graft failure. Univariate analysis showed recipients with postoperative pneumonia (p<0.05), acute hepatic necrosis, and intensive care unit (ICU) stay ≥7 days (both p<0.01), postoperative bacteremia, creatinine on day 3 after LT>2 mg/dL, and alanine transaminase on day 1 after LT >1800 μmol/L (all P<0.001) were much more likely to die.

Conclusions

In-hospital mortality of LT recipients was high, due in large part to infections. Acute hepatic necrosis, prolonged post-transplant ICU stays, certain types of postoperative infections, and postoperative liver and kidney dysfunction were potential risk factors for in-hospital mortality of LT recipients.

Caring for Hospitalized Patients with Diabetes Mellitus, Hyperglycemia, and COVID-19: Bridging the Remaining Knowledge Gaps

PURPOSE OF REVIEW

This review discusses the interplay between coronavirus disease 2019 (COVID-19, caused by SARS-CoV-2 infection), diabetes mellitus, and hyperglycemia in the hospital setting. There are data emerging about diabetes and hyperglycemia, their prevalence, and potential risks in the setting of SARS-CoV-2 infection and COVID-19.

RECENT FINDINGS

It is known that viral infections exert effects on beta cell function and insulin resistance. Therefore, much can be learned about SARS-CoV-2/COVID-19 from examining these known relationships. Such pathophysiological underpinnings may unlock greater understanding as we navigate atypical cases of hyperglycemia, severe insulin resistance, and diabetic ketoacidosis amidst COVID-19. Glycemic outcomes likely have beneficial effects on morbidity and mortality, but this needs to be studied. Changes in diabetes-related protocols and new technology can be deployed in the inpatient setting to potentially improve healthcare worker and patient safety; however, one must weigh the risks and benefits of implementation during a pandemic. Ultimately, knowledge and research must be shared at record speed to combat this global crisis.

Perioperative diabetes care

People with diabetes occupy approximately 18% of all acute inpatient hospital beds in the UK, compared with 6.5% of the general population. For those undergoing surgery, having diabetes is known to be associated with increased harms, however harm is defined. For those undergoing elective surgery, there is a defined patient journey, starting with referral from primary care to surgical outpatients, then onto preoperative assessment clinic before being admitted for surgery, and then from recovery through to discharge home. Because of the multiple causes for possible harm, communication between members of the healthcare team at each stage of this journey and with the person with diabetes is essential.Recently, the National Confidential Enquiry into Patient Outcomes and Death has shown that the care of people with diabetes undergoing surgery needs to be improved, and they have made several recommendations that trusts should adopt to minimise the harms in this vulnerable population.

Intraoperative glycemic control in patients undergoing Orthotopic liver transplant: a single center prospective randomized study

Background

Perioperative hyperglycemia is associated with poor outcomes yet evidence to guide intraoperative goals and treatment modalities during non-cardiac surgery are lacking. End-stage liver disease is associated with altered glucose homeostasis; patients undergoing liver transplantation display huge fluctuations in blood glucose (BG) and represent a population of great interest. Here, we conduct a randomized trial to compare the effects of strict versus conventional glycemic control during orthotopic liver transplant (OLT).

Methods

Following approval by the Institutional Review Board of the University of Michigan Medical School and informed consent, 100 adult patients undergoing OLT were recruited. Patients were randomized to either strict (target BG 80–120 mg/dL) or conventional (target BG 180–200 mg/dL) BG control with block randomization for diabetic and nondiabetic patients. The primary outcomes measured were 1-year patient and graft survival assessed on an intention to treat basis. Graft survival is defined as death or needing re-transplant (www.unos.org). Three and 5-year patient and graft survival, infectious and biliary complications were measured as secondary outcomes. Data were examined using univariate methods and Kaplan-Meir survival analysis. A sensitivity analysis was performed to compare patients with a mean BG of ≤120 mg/dL and those > 120 mg/dL regardless of treatment group.

Results

There was no statistically significant difference in patient survival between conventional and strict control respectively;1 year, 88% vs 88% (p-0.99), 3 years, 86% vs 84% (p- 0.77), 5 years, 82% vs 78. % (p-0.36). Graft survival was not different between conventional and strict control groups at 1 year, 88% vs 84% (p-0.56), 3 years 82% vs 76% (p-0.46), 5 years 78% vs 70% (p-0.362).

Conclusion

There was no difference in patient or graft survival between intraoperative strict and conventional glycemic control during OLT.

Trial registration

Clinical trial number and registry: www.clinicaltrials.gov NCT00780026. This trial was retrospectively registered on 10/22/2008.

Updates in Glycemic Management in the Hospital

PURPOSE OF REVIEW

To provide an update of glycemic management during metabolic stress related to surgery or critical illness.

RECENT FINDINGS

There is a clear association between severe hyperglycemia, hypoglycemia, and high glycemic variability and poor outcomes of postoperative or critically ill patients. However, the impressive beneficial effects of tight glycemic management (TGM) by intensive insulin therapy reported in one study were never reproduced. Hence, the recommendation of TGM is now replaced by more liberal blood glucose (BG) targets (< 180 mg/dL or 10 mM). Recent data support the concept of targeting individualized blood glucose (BG) values according to the presence of diabetes mellitus/chronic hyperglycemia, the presence of brain injury, and the time from injury. A more liberal glycemic management goal is currently advised during metabolic stress and could be switched to individualized glycemic management once validated by prospective trials.

Recent advances in diabetes treatments and their perioperative implications

PURPOSE OF REVIEW

The implications for perioperative management of new oral antihyperglycemic medications and new insulin treatment technologies are reviewed.

RECENT FINDINGS

The preoperative period represents an opportunity to optimize glycemic control and potentially to reduce adverse outcomes. There is now general consensus that the optimal blood glucose target for hospitalized patients is approximately 106-180 mg/dl (6-10 mmol/l). Recommendations for the management of antihyperglycemic medications vary among national guidelines. It may not be necessary to cease all antihyperglycemic agents prior to surgery. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are associated with higher rates of ketoacidosis especially in acutely unwell and postsurgical patients. The clinical practice implications of new insulin formulations, and new systems for insulin delivery, are not clear. The optimal perioperative management of these will vary depending on local institutional factors such as staff skills and existing clinical practices. Improved hospital care delivery standards, quality assurance, process improvements, consistency in clinical practice, and coordinated multidisciplinary teamwork should be a major focus for improving outcomes of perioperative patients with diabetes.

SUMMARY

Sulfonylureas and SGLT2i should be ceased before moderate or major surgery. Other oral antihyperglycemic therapies may be continued or ceased. Complex patients and/or new therapies require specialized multidisciplinary management.

Hyperglycemia in the Posttransplant Period: NODAT vs Posttransplant Diabetes Mellitus

Objective

To characterize the types of hyperglycemia that occur up to 1 year following liver transplant and to clarify the nomenclature for posttransplant hyperglycemia.

Design

We analyzed 1-year glycemic follow-up data in 164 patients who underwent liver transplant and who had been enrolled in a randomized controlled trial comparing moderate to intensive insulin therapy to determine if patients had preexisting known diabetes, transient hyperglycemia, persistent hyperglycemia, or new-onset diabetes after transplantation (NODAT).

Results

Of 119 patients with posttransplant hyperglycemia following hospital discharge, 49 had preexisting diabetes, 5 had insufficient data for analysis, 48 had transient hyperglycemia (16 resolved within 30 days and 32 resolved between 30 days and 1 year), 13 remained persistently hyperglycemic out to 1 year and most likely had preexisting diabetes that had not been diagnosed or insulin resistance/insulinopenia prior to transplant, and 4 had NODAT (i.e., patients with transient hyperglycemia after transplant that resolved but then later truly developed sustained hyperglycemia, meeting criteria for diabetes).

Conclusions

Distinct categories of patients with hyperglycemia following organ transplant include known preexisting diabetes, persistent hyperglycemia (most likely unknown preexisting diabetes or insulin resistance/insulinopenia), transient hyperglycemia, and NODAT. Those with preexisting diabetes for many years prior to transplant may well have very different long-term outcomes compared with those with true NODAT. Therefore, it would be prudent to classify patients more carefully. Long-term outcome studies are needed to determine if patients with true NODAT have the same poor prognosis as patients with preexisting diabetes (diagnosed and undiagnosed) undergoing transplant.

Development of a Predictive Model for Hyperglycemia in Nondiabetic Recipients After Liver Transplantation

Background

Posttransplant hyperglycemia has been associated with increased risks of transplant rejection, infections, length of stay, and mortality.

Methods

To establish a predictive model to identify nondiabetic recipients at risk for developing postliver transplant (LT) hyperglycemia, we performed this secondary, retrospective data analysis of a single-center, prospective, randomized, controlled trial of glycemic control among 107 adult LT recipients in the inpatient period. Hyperglycemia was defined as a posttransplant glucose level greater than 200 mg/dL after initial discharge up to 1 month following surgery. Candidate variables with P less than 0.10 in univariate analyses were used to build a multivariable logistic regression model using forward stepwise selection. The final model chosen was based on statistical significance and additive contribution to the model based on the Bayesian Information Criteria.

Results

Forty-three (40.2%) patients had at least 1 episode of hyperglycemia after transplant after the resolution of the initial postoperative hyperglycemia. Variables selected for inclusion in the model (using model optimization strategies) included length of hospital stay (odds ratio [OR], 0.83; P < 0.001), use of glucose-lowering medications at discharge (OR, 3.76; P = 0.03), donor female sex (OR, 3.18; P = 0.02) and donor white race (OR, 3.62; P = 0.01). The model had good calibration (Hosmer-Lemeshow goodness-of-fit test statistic = 9.74, P = 0.28) and discrimination (C-statistic = 0.78; 95% confidence interval, 0.65-0.81, bias-corrected C-statistic = 0.78).

Conclusions

Shorter hospital stay, use of glucose-lowering medications at discharge, donor female sex and donor white race are important determinants in predicting hyperglycemia in nondiabetic recipients after hospital discharge up to 1 month after liver transplantation.

Postoperative tight glycemic control significantly reduces postoperative infection rates in patients undergoing surgery: a meta-analysis

BACKGROUND

The benefit results of postoperative tight glycemic control (TGC) were controversial and there was a lack of well-powered studies that support current guideline recommendations.

METHODS

The EMBASE, MEDLINE, and the Cochrane Library databases were searched utilizing the key words "Blood Glucose", "insulin" and "Postoperative Period" to retrieve all randomized controlled trials evaluating the benefits of postoperative TGC as compared to conventional glycemic control (CGC) in patients undergoing surgery.

RESULTS

Fifteen studies involving 5053 patients were identified. As compared to CGC group, there were lower risks of total postoperative infection (9.4% vs. 15.8%; RR 0.586, 95% CI 0.504 to 0.680, p <  0.001) and wound infection (4.6% vs. 7.2%; RR 0.620, 95% CI 0.422 to 0.910, p = 0.015) in TGC group. TGC also showed a lower risk of postoperative short-term mortality (3.8% vs. 5.4%; RR 0.692, 95% CI 0.527 to 0.909, p = 0.008), but sensitivity analyses showed that the result was mainly influenced by one study. The patients in the TGC group experienced a significant higher rate of postoperative hypoglycemia (22.3% vs. 11.0%; RR 3.145, 95% CI 1.928 to 5.131, p <  0.001) and severe hypoglycemia (2.8% vs. 0.7%; RR 3.821, 95% CI 1.796 to 8.127, p <  0.001) as compared to CGC group. TGC showed less length of ICU stay (SMD, - 0.428 days; 95% CI, - 0.833 to - 0.022 days; p = 0.039). However, TGC showed a neutral effect on neurological dysfunction (1.1% vs. 2.4%; RR 0.499, 95% CI 0.219 to 1.137, p = 0.098), acute renal failure (3.3% vs. 5.4%, RR 0.610, 95% CI 0.359 to 1.038, p = 0.068), duration of mechanical ventilation (p = 0.201) and length of hospitalization (p = 0.082).

CONCLUSIONS

TGC immediately after surgery significantly reduces total postoperative infection rates and short-term mortality. However, it might limit conclusion regarding the efficacy of TGC for short-term mortality in sensitivity analyses. The patients in the TGC group experienced a significant higher rate of postoperative hypoglycemia. This study may suggest that TGC should be administrated under close glucose monitoring in patients undergoing surgery, especially in those with high postoperative infection risk.

Risk assessment of the hospital discharge process of high-risk patients with diabetes

Objectives

Describe the application of a risk assessment to identify failures in the hospital discharge process of a high-risk patient group, liver transplant (LT) recipients with diabetes mellitus (DM) and/or hyperglycaemia who require high-risk medications.

Design

A Failure Modes, Effects and Criticality Analysis (FMECA) of the hospital discharge process of LT recipients with DM and/or hyperglycaemia who required DM education and training before discharge was conducted using information from clinicians, patients and data extraction from the electronic health records (EHR). Failures and their causes were identified and the frequency and characteristics (harm, detectability) of each failure were assigned using a score of low/best (1) to high/worst (10); a Criticality Index (CI=Harm×Frequency) and a Risk Priority Number (RPN=Harm×Frequency×Detection) were also calculated.

Setting

An academic, tertiary care centre in Chicago, Illinois.

Participants

Healthcare providers (N=31) including physicians (n= 6), advanced practice providers (n=12), nurses (n=6), pharmacists (n= 4), staff (n=3) and patients (n=6) and caregivers (n=3) participated in the FMECA; EHR data for LT recipients with DM or hyperglycaemia (N=100) were collected.

Results

Of 78 identified failures, the most critical failures (n=15; RPNs=700, 630, 560; CI=70) were related to variability in delivery of diabetes education and training, care coordination and medication prescribing patterns of providers. Underlying causes included timing of patient education, lack of assessment of patients’ knowledge and industry-level design failures of healthcare products (eg, EHR, insulin pen).

Conclusion

Most identified critical failures are preventable and suggest the need for the design of interventions, informed by the failures identified by this FMECA, to mitigate safety risks and improve outcomes of high-risk patient populations.

Post-Liver Transplantation Diabetes Mellitus: A Review of Relevance and Approach to Treatment

Post-liver transplantation diabetes mellitus (PLTDM) develops in up to 30% of liver transplant recipients and is associated with increased risk of mortality and multiple morbid outcomes. PLTDM is a multicausal disorder, but the main risk factor is the use of immunosuppressive agents of the calcineurin inhibitor (CNI) family (tacrolimus and cyclosporine). Additional factors, such as pre-transplant overweight, nonalcoholic steatohepatitis and hepatitis C virus infection, may further increase risk of developing PLTDM. A diagnosis of PLTDM should be established only after doses of CNI and steroids are stable and the post-operative stress has been overcome. The predominant defect induced by CNI is insulin secretory dysfunction. Plasma glucose control must start immediately after the transplant procedure in order to improve long-term results for both patient and transplant. Among the better known antidiabetics, metformin and DPP-4 inhibitors have a particularly benign profile in the PLTDM context and are the preferred oral agents for long-term management. Insulin therapy is also an effective approach that addresses the prevailing pathophysiological defect of the disorder. There is still insufficient evidence about the impact of newer families of antidiabetics (GLP-1 agonists, SGLT-2 inhibitors) on PLTDM. In this review, we summarize current knowledge on the epidemiology, pathogenesis, course of disease and medical management of PLTDM.

Diabetes Mellitus Following Renal Transplantation: Clinical and Pharmacological Considerations for the Elderly Patient

Post-transplant diabetes mellitus occurs in 30-50% of cases during the first year post-renal transplantation. It is associated with increased morbidity, mortality and healthcare costs. Risk factors include age and specific immunosuppression regimens. At the same time, renal transplantation is increasingly indicated in elderly (aged >65 years) patients as this proportion of older patients in the prevalent dialysis population has increased. The immune system and β cells undergo senescence and this impacts on the risk for developing post-transplant diabetes and our ability to prevent such development. It may, however, be possible to identify patients at risk of developing post-transplant diabetes, enabling treatment protocols that prevent or reduce the impact of post-transplant diabetes. Much work remains to be completed in this area and is facilitated by the growing base of knowledge regarding the pathophysiology of post-transplant diabetes. Should post-transplant diabetes develop, there are a range of treatment options available. There is increasing interest in using newer agents, although their safety and efficacy in transplant recipients remains to be conclusively established.