WellDoc mobile diabetes management randomized controlled trial: change in clinical and behavioral outcomes and patient and physician satisfaction.

BACKGROUND: Less than 63% of individuals with diabetes meet professional guidelines target of hemoglobin A1c <7.0%, and only 7% meet combined glycemic, lipid, and blood pressure goals. The primary study aim was to assess the impact on A1c of a cell phone-based diabetes management software system used with web-based data analytics and therapy optimization tools. Secondary aims examined health care provider (HCP) adherence to prescribing guidelines and assessed HCPs' adoption of the technology. METHODS: Thirty patients with type 2 diabetes were recruited from three community physician practices for a 3-month study and evenly randomized. The intervention group received cell phone-based software designed by endocrinologists and CDEs (WellDoc Communications, Inc., Baltimore, MD). The software provided real-time feedback on patients' blood glucose levels, displayed patients' medication regimens, incorporated hypo- and hyperglycemia treatment algorithms, and requested additional data needed to evaluate diabetes management. Patient data captured and transferred to secure servers were analyzed by proprietary statistical algorithms. The system sent computer-generated logbooks (with suggested treatment plans) to intervention patients' HCPs. RESULTS: The average decrease in A1c for intervention patients was 2.03%, compared to 0.68% (P < 0.02, one-tailed) for control patients. Of the intervention patients, 84% had medications titrated or changed by their HCP compared to controls (23%, P = 0.002). Intervention patients' HCPs reported the system facilitated treatment decisions, provided organized data, and reduced logbook review time. CONCLUSIONS: Adults with type 2 diabetes using WellDoc's software achieved statistically significant improvements in A1c. HCP and patient satisfaction with the system was clinically and statistically significant.

Digital Health Care by In Silico Glycation of HbA1 Blood Cells.

BACKGROUND: Diabetes health care relies on the HbA1c (A1c) assay and associated average glucose (AG) to evaluate and control chronic glycemia. However, the A1c assay is plagued with significant noise, lag time, and specificity issues. Current studies support the significant health care advantage of clinical action based on real-time blood glucose (BG) metrics. We seek to improve diabetes management by directly relating such metrics to AG levels as mediated by recently discovered recurrent endocrine cycles. METHODS: Several studies collected multiple months of BG data on 111 subjects totaling 261 893 CGM measurements and 29 278 meter readings. These data are a rich source of multiday metrics in terms of the CGM and SMBG daily profiles. The recurrent endocrine patterns expose key metric relationships for monitoring AG related to A1c using CGM and SMBG data. Consequently, day-to-day tracking of AG is expressed as a simple two-parameter function of fasting BG for all studies. RESULTS: Consequently, when applied to 2518 qualified days of 64 subjects, the function predicts daily AG values with 2% relative standard error. All studies produced compatible results. By restricting one parameter to a constant, the error increased to 3%. CONCLUSIONS: The recurrent endocrine patterns revealed a persistent structure hidden within the multiday fluctuations that becomes a simple meter-compatible equation that accurately measures real-time trending of AG using fasting BG values. This enables a digital health monitoring service and self-monitoring device that reveals immediate disease progression as well as the impact of interventions and medications better than possible with the A1c assay.

Microarray quality control.

Physically separated groups of specific sequences (probes) provide useful high through put (HTP) measurements for the amount of selected DNA/RNA sequences in a biological target sample. Unfortunately, these measurements are impacted by various technical sources, such as platform production factors, target preparation processes, hybridization method/conditions, and signal-extraction devices and methods. Given the typically huge population of signals, statistical methods are especially effective at estimation and removal of such technical distortions (Churchill, 2002; Kerr et al., 2000; Yue et al., 2001), as well as providing metrics for computer-based quality control (QC), for example, autoQC (Minor et al., 2002a). This chapter reviews statistical procedures that have been validated by successful applications in both large-scale commercial ventures (Ganter et al., 2005) and individual research studies (Parisi et al., 2003, 2004) involving HTP projects. This chapter focuses on methods for spatially distributed probes on a flat medium surface such as glass, collectively known as a microarray.

Recurrent Endocrine Cycles.

BACKGROUND: The chaotic nature of blood glucose creates a formidable clinical challenge for diabetes healthcare. The recent discovery of recurrent endocrine cycles offers the advantage of advanced-prediction (proactive) health care. METHODS: Historical studies covering 111 patients and 1 subject collected several months of glucose readings and their daily metrics. Phase portraits and phase analytics can detect recurrent metric cycles and test their ability to anticipate serious glycemic conditions. RESULTS: Recurrent patterns were detected having a rate of ~7 days per complete cycle. Plots and risk models based on these cycles produced advanced alerts for acute glycemia, capturing greater than 96% of true-positive days with a 5% false-positive rate. CONCLUSIONS: This method can be implemented graphically and functionally within a BG monitoring system to warn doctors and patients of impending serious glycemic levels.

Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action.

Successful drug discovery requires accurate decision making in order to advance the best candidates from initial lead identification to final approval. Chemogenomics, the use of genomic tools in pharmacology and toxicology, offers a promising enhancement to traditional methods of target identification/validation, lead identification, efficacy evaluation, and toxicity assessment. To realize the value of chemogenomics information, a contextual database is needed to relate the physiological outcomes induced by diverse compounds to the gene expression patterns measured in the same animals. Massively parallel gene expression characterization coupled with traditional assessments of drug candidates provides additional, important mechanistic information, and therefore a means to increase the accuracy of critical decisions. A large-scale chemogenomics database developed from in vivo treated rats provides the context and supporting data to enhance and accelerate accurate interpretation of mechanisms of toxicity and pharmacology of chemicals and drugs. To date, approximately 600 different compounds, including more than 400 FDA approved drugs, 60 drugs approved in Europe and Japan, 25 withdrawn drugs, and 100 toxicants, have been profiled in up to 7 different tissues of rats (representing over 3200 different drug-dose-time-tissue combinations). Accomplishing this task required evaluating and improving a number of in vivo and microarray protocols, including over 80 rigorous quality control steps. The utility of pairing clinical pathology assessments with gene expression data is illustrated using three anti-neoplastic drugs: carmustine, methotrexate, and thioguanine, which had similar effects on the blood compartment, but diverse effects on hepatotoxicity. We will demonstrate that gene expression events monitored in the liver can be used to predict pathological events occurring in that tissue as well as in hematopoietic tissues.