Primary Outcome Measures:
- Derive optimum Self Monitored Blood Glucose (SMBG) testing patterns from CGM data. CGM data will be graphically and statistically represented by an Ambulatory Glucose Profile (AGP). [ Time Frame: 2 weeks of CGM data ]
The investigators propose to employ CGM technology in subjects using insulin sensitizers, incretin mimetics and potentiators, insulin secretagogues and insulin to identify the minimum episodic data set that closely mimics CGM data. Specifically, the investigators propose to employ blinded CGM for 2 weeks to capture the diurnal glucose patterns of individuals on different therapeutic regimens. The investigators propose to examine these data to determine whether a specific pattern of SMBG testing derived from these data would provide the same information (e.g., detection of hypoglycemia, measures of glycemic exposure, variability and stability) not possible by relying solely on current SMBG testing schedules.
In preparation for this study the investigators developed a matrix using data collected from previous studies. The matrix summarizes the amount of CGM data the investigators currently possess and indicates where the investigators lack sufficient data to produce SMBG testing schedules based on CGM data. It was determined that 6 cases were needed for each therapy group. An underlying question is whether after the investigators fill this matrix the investigators can discover a common testing pattern for SMBG that would provide sufficient information to uncover the myriad perturbations in glucose patterns that characterize type 2 diabetes. To assure that the investigators are collecting adequate monitoring data, the investigators propose to use Ambulatory Glucose Profile (AGP) analysis of blinded CGM data. This novel approach will enable us to answer the question as to the optimum time and number of SMBG tests sufficient to guide clinical decisions.