MEtabolic and Renal Effects of AutoMAted Insulin Delivery Systems in Youth With Type 1 Diabetes Mellitus

Summary

Participation Requirements

Description

Background: Over 1.25 million Americans have type 1 diabetes mellitus (T1DM), significantly increasing the risk of early death from cardio-renal disease. Per the American Diabetes Association, only 14% of children with T1DM meet glycemic targets [Wood et al. Diabetes Care 2013; 36:2035-37]. This is ...

Background: Over 1.25 million Americans have type 1 diabetes mellitus (T1DM), significantly increasing the risk of early death from cardio-renal disease. Per the American Diabetes Association, only 14% of children with T1DM meet glycemic targets [Wood et al. Diabetes Care 2013; 36:2035-37]. This is a severe and pervasive problem, as a child diagnosed with T1DM today is expected to live up to 17 years less than non-diabetic peers. It is established that time outside of goal glycemic target range increases the likelihood of developing micro- and macro-vascular diabetic complications including diabetic kidney disease (DKD) and cardiovascular disease (CVD). However, metabolic risk factors beyond glycemic control including insulin resistance and obesity are also increasingly recognized to contribute to the increased risk of DKD and CVD. Automated insulin delivery (AID) systems such as the hybrid closed loop artificial pancreas (HCL AP) combine use of an insulin pump, continuous glucose monitor (CGM), and a control algorithm to adjust background insulin delivery to improve time in target range. AID systems such as the predictive low glucose suspend (PLGS) system pause insulin delivery to try to reduce hypoglycemia. AID systems are now seeing markedly increased commercial use; however, the long-term effects on insulin sensitivity, body mass index (BMI), cardio-metabolic markers, and kidney function have not yet been studied. Preliminary basic science research suggests that periods of rest from insulin exposure provided by AID systems may have positive effects on DKD and CVD risk. In this proposal we intend to investigate the gap in knowledge between glycemic changes seen with AID systems and the impact on markers of long-term complications. Specific Aims and Hypotheses: Specific Aim 1: To examine the effects of the AID systems on glycemic control and insulin sensitivity as compared to traditional insulin pumps and multiple daily injections in youth with T1DM Hypothesis 1.1: Treatment with the AID systems improves glycemic control in youth with T1DM Hypothesis 1.2: Treatment with the AID systems increases insulin sensitivity and decreases insulin requirement in youth with T1DM Specific Aim 2: To examine the effects of the AID systems on kidney function and metabolic markers as compared to traditional insulin pumps and multiple daily injections in youth with T1DM Hypothesis 2.1: Treatment with the AID systems improves metabolic markers in youth with T1DM Hypothesis 2.2: Treatment with the AID systems improves kidney function in youth with T1DM Design: This study is a pilot study aimed at recruiting youth ages 7 to 18 years from the following 3 groups with T1DM: control participants on either multiple daily injections or conventional pump therapy, youth being transitioned to a HCL AP system, and youth being transitioned to a PLGS system. Exclusion criteria include non-T1DM, non-insulin blood glucose altering medications, pregnancy, breastfeeding, or a ketogenic diet. We plan to complete a physical exam with pubertal staging, collect information on recent insulin usage and dosages, fasting serum and urine samples, and a DXA scan before the participant transitions to either a HCL AP or a PLGS system, if applicable. Following 3-6 months of treatment we will then collect the identical data as at baseline. Outcome measures include CGM data, total daily insulin dose, time suspended from insulin delivery, height, weight, BMI, waist circumference, hip circumference, blood pressure, HbA1c, c-peptide, total cholesterol, HDL, LDL, triglycerides, adiponectin, and DXA scan to evaluate cardio-metabolic markers and calculate insulin sensitivity, as well as serum creatinine, cystatin c, copeptin, and urine microalbumin to evaluate kidney health and calculate GFR by Zappitelli and FAS equations.

Tracking Information