An Investigation of the Effects of Different Dialysis Prescriptions on Systemic Glucose Metabolism and Mortality in Non-diabetic Peritoneal Dialysis Patients
An Investigation of the Effects of Different Dialysis Prescriptions on Systemic Glucose Metabolism and Mortality in Non-diabetic Peritoneal Dialysis Patients
Peritoneal Dialysis (PD) is a commonly used treatment for end stage renal failure, and the most commonly used dialysate contains unphysiological amounts of glucose, a high proportion of which is absorbed. Recent analysis of the Global Fluid Study, has established that in non-diabetic prevalent patients on PD a random glucose level is dependent on dialysate glucose load and is a predictor of death. By utilising clinical data and additional biosamples collected for the NIHR funded PD-CRAFT study, the investigators aim to confirm this finding, define the relationship between dialysate glucose exposure according to prescription regimes and glycaemia, define the most useful biomarker to monitor glycaemia , and establish the role that impaired insulin sensitivity plays in blood glucose levels. Furthermore the investigators will explore the hypothesis that insulin resistance is associated with disturbance of the carnitine/acetyl-carnitine equilibrium that might benefit from intra-peritoneal carnitine supplementation.
PD-CRAFT is an observational cohort study of 3000 prevalent PD patients collecting detailed clinical data, including glucose exposure and samples of dialysate. and blood which will be stored in the UK BioCentre. Follow up is for up to 2 years or endpoint (death, technique failure).
Multivariate regression will be used to establish determinants of the non-fasting blood glucose and other measures of glycaemia, in particular different dialysis regimes, (e.g. modality, dwell lengths, fill volumes, and dialysate type and concentration specifically seeking to identify prescriptions that minimise the systemic effects) combined with measures of insulin resistance. The investigators will establish whether blood glucose predicts survival in an adjusted analysis (~300 endpoints needed) using Cox regression and explore the relationship of other biomarkers to survival.
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