Dynamics of glucose metabolism after liver transplantation: Prediabetes as a window of opportunity for patient survival and long-term kidney function

Differential risk assessment in persons at risk of type 2 diabetes using urinary peptidomics

OBJECTIVE

Individuals at increased risk of type 2 diabetes have recently been classified into six prediabetes clusters, which stratify the risk of progression to diabetes and diabetes complications. Clusters 1, 2 and 4 are low-risk clusters while clusters 3, 5 and 6 are high-risk clusters; individuals in cluster 6 have an elevated risk of nephropathy and all-cause mortality despite delayed onset of diabetes. The urinary peptidome classifiers CKD273 (chronic kidney disease, CKD), HF2 (heart failure, HF) and CAD238 (coronary artery disease, CAD) are based on unique urinary peptide patterns and have shown potential for identifying individuals at risk for CKD and cardiovascular pathologies. This observational study investigates whether peptidome classifiers can differentiate complication risks across the prediabetes clusters and if a novel combination of peptides can distinguish high-risk from low-risk prediabetes clusters.

METHODS

Urine peptidome analysis was performed on spot urine samples from individuals across 6 prediabetes clusters (n = 249) and 19 individuals with screen-detected diabetes (study cohorts at University Hospital Tübingen, Germany from 11/2004 to 11/2012). Predefined urinary classifiers were calculated for each participant. Lasso regression analysis was used to identify an optimal combination of peptides distinguishing low- Schlesinger et al. (2022), Wagner et al. (2021) [1,2,4] and high-risk (Rooney et al., 2021; Wagner, 2023; Latosinska et al., 2021 [3,5,6]) clusters.

RESULTS

The predefined urinary peptidome classifiers CKD273, HF2 and CAD238 differed significantly across prediabetes clusters, particularly with elevated values in cluster 6 compared to the healthiest cluster 2. CKD273, HF2 and CAD238 were inversely associated with insulin sensitivity indexes. Machine Learning identified a combination of 112 urinary peptides that differentiated low-risk from high-risk prediabetes clusters (AUC-ROC 0.868 (95 % CI 0.755-0.981)).

CONCLUSIONS

Urinary peptidome classifiers support the increased risk of CKD and suggest an elevated risk of heart failure and coronary artery disease in the high-risk prediabetes cluster 6. Urine peptidomics show promising potential as a tool for identifying high-risk prediabetes individuals and guiding early preventive interventions.

Development and Validation of a New Score to Assess the Risk of Posttransplantation Diabetes Mellitus in Kidney Transplant Recipients

Background

Posttransplantation diabetes mellitus (PTDM) is a serious complication of solid organ transplantation. It is associated with major adverse cardiovascular events, which are a leading cause of morbidity and mortality in transplant patients. This study aimed to develop and validate a score to predict the risk of PTDM in kidney transplant recipients.

Methods

A single-center retrospective cohort study was conducted in a tertiary care hospital in Medellín, Colombia, between 2005 and 2019. Data from 727 kidney transplant recipients were used to develop a risk prediction model. Significant predictors with competing risks were identified using time-dependent Cox proportional hazard regression models. To build the prediction model, the score for each variable was weighted using calculated regression coefficients. External validation was performed using independent data, including 198 kidney transplant recipients from Tübingen, Germany.

Results

Among the 727 kidney transplant recipients, 122 developed PTDM. The predictive model was based on 5 predictors (age, gender, body mass index, tacrolimus therapy, and transient posttransplantation hyperglycemia) and exhibited good predictive performance (C-index: 0.7 [95% confidence interval, 0.65-0.76]). The risk score, which included 33 patients with PTDM, was used as a validation data set. The results showed good discrimination (C-index: 0.72 [95% confidence interval, 0.62-0.84]). The Brier score and calibration plot demonstrated an acceptable fit capability in external validation.

Conclusions

We proposed and validated a prognostic model to predict the risk of PTDM, which performed well in discrimination and calibration, and is a simple score for use and implementation by means of a nomogram for routine clinical application.

Interventions Against Posttransplantation Diabetes: A Scientific Rationale for Treatment Hierarchy Based on Literature Review

Posttransplant diabetes (PTD) is a common medical complication after solid organ transplantation. Because of adverse outcomes associated with its development and detrimental impact on long-term survival, strategies to prevent or manage PTD are critically important but remain underresearched. Treatment hierarchies of antidiabetic therapies in the general population are currently being revolutionized based on cardiovascular outcome trials, providing evidence-based rationale for optimization of medical management. However, opportunities for improving medical management of PTD are challenged by 2 important considerations: (1) translating clinical evidence data from the general population to underresearched solid organ transplant cohorts and (2) targeting treatment based on primary underlying PTD pathophysiology. In this article, the aim is to provide an overview of PTD treatment options from a new angle. Rationalized by a consideration of underlying PTD pathophysiological defects, which are heterogeneous among diverse transplant patient cohorts, a critical appraisal of the published literature and summary of current research in progress will be reviewed. The aim is to update transplant professionals regarding medical management of PTD from a new perspective tailored therapeutic intervention based on individualized characteristics. As the gap in clinical evidence between management of PTD versus type 2 diabetes widens, it is imperative for the transplant community to bridge this gap with targeted clinical trials to ensure we optimize outcomes for solid organ transplant recipients who are at risk or develop PTD. This necessary clinical research should help efforts to improve long-term outcomes for solid transplant patients from both a patient and graft survival perspective.