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Biomedical subjects

William L Clarke

Publications and source records attributed to William L Clarke.

9 recordsLinked to original sources
PubMed2006

Predictors of fear of hypoglycemia in adolescents with type 1 diabetes and their parents.

OBJECTIVES: This study tested the hypothesis that both trait anxiety and hypoglycemic history contribute to fear of hypoglycemia (FOH) both in adolescents with type 1 diabetes mellitus (T1DM) and in their parents, and relationships between FOH and other variables including metabolic control, symptom perception, and use of insulin pump therapy. STUDY DESIGN: Thirty-nine parent-adolescent pairs completed questionnaires assessing background and clinical information, hypoglycemic episodes, FOH, and trait anxiety. Adolescent blood was also sampled for glycosylated hemoglobin A1c (HbA1c) measurement. RESULTS: In adolescents, both trait anxiety and frequency of severe hypoglycemic episodes were significant predictors of FOH, together accounting for almost 50% of the variance. Parental FOH was not predicted by their own trait anxiety or their child's hypoglycemic history but by whether they believed that their child carried emergency glucose. FOH was not related to metabolic control, although adolescents who experienced recent severe hypoglycemia (SH) with unconsciousness had significantly higher HbA1c. Parental trait anxiety significantly correlated with child trait anxiety, but parent-child levels of FOH were unrelated. Neither trait anxiety nor FOH related to reported symptoms, and FOH was not lower in parents with insulin pump therapy. CONCLUSIONS: Consistent with findings from adult patient populations, trait anxiety levels and recent experiences with hypoglycemia predict FOH in adolescents with T1DM. In parents, however, beliefs about their adolescents' ability to cope with hypoglycemic episodes predicted FOH. FOH in adolescents with T1DM and their parents is a complex construct influenced by multiple personality and situational and behavioral factors, and its impact on diabetes management remains unclear.

Adolescent↗
PubMed2005

Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application.

BACKGROUND: Continuous glucose monitors (CGMs) collect detailed blood glucose (BG) time series, which carry significant information about the dynamics of BG fluctuations. In contrast, the methods for analysis of CGM data remain those developed for infrequent BG self-monitoring. As a result, important information about the temporal structure of the data is lost during the translation of raw sensor readings into clinically interpretable statistics and images. METHODS: The following mathematical methods are introduced into the field of CGM data interpretation: (1) analysis of BG rate of change; (2) risk analysis using previously reported Low/High BG Indices and Poincare (lag) plot of risk associated with temporal BG variability; and (3) spatial aggregation of the process of BG fluctuations and its Markov chain visualization. The clinical application of these methods is illustrated by analysis of data of a patient with Type 1 diabetes mellitus who underwent islet transplantation and with data from clinical trials. RESULTS: Normative data [12,025 reference (YSI device, Yellow Springs Instruments, Yellow Springs, OH) BG determinations] in patients with Type 1 diabetes mellitus who underwent insulin and glucose challenges suggest that the 90%, 95%, and 99% confidence intervals of BG rate of change that could be maximally sustained over 15-30 min are [-2,2], [-3,3], and [-4,4] mg/dL/min, respectively. BG dynamics and risk parameters clearly differentiated the stages of transplantation and the effects of medication. Aspects of treatment were clearly visualized by graphs of BG rate of change and Low/High BG Indices, by a Poincare plot of risk for rapid BG fluctuations, and by a plot of the aggregated Markov process. CONCLUSIONS: Advanced analysis and visualization of CGM data allow for evaluation of dynamical characteristics of diabetes and reveal clinical information that is inaccessible via standard statistics, which do not take into account the temporal structure of the data. The use of such methods improves the assessment of patients' glycemic control.

Adult↗
PubMed2005

Relationships between hyperglycemia and cognitive performance among adults with type 1 and type 2 diabetes.

OBJECTIVE: Hyperglycemia is a common event among patients with type 1 and type 2 diabetes. While the cognitive-motor slowing associated with hypoglycemia is well documented, the acute effects of hyperglycemia have not been studied extensively, despite patients' reports of negative effects. This study prospectively and objectively assessed the effects of hyperglycemia on cognitive-motor functioning in subjects' natural environment. RESEARCH DESIGN AND METHODS: Study 1 investigated 105 adults with type 1 diabetes (mean age 37 years and mean duration of diabetes 20 years), study 2 investigated 36 adults with type 2 diabetes (mean age 50 years and mean duration of diabetes 10 years), and study 3 investigated 91 adults with type 1 diabetes (mean age 39 years and mean duration of diabetes 20 years). Subjects used a hand-held computer for 70 trials over 4 weeks, which required them to complete various cognitive-motor tasks and then measure and enter their current blood glucose reading. RESULTS: Hyperglycemia (blood glucose >15 mmol/l) was associated with slowing of all cognitive performance tests (P < 0.02) and an increased number of mental subtraction errors for both type 1 and type 2 diabetic subjects. The effects of hyperglycemia were highly individualized, impacting approximately 50% of the subjects. CONCLUSIONS: Acute hyperglycemia is not a benign event for many individuals with diabetes, but it is associated with mild cognitive dysfunction.

Adult↗
PubMed2005

Evaluating the clinical accuracy of two continuous glucose sensors using continuous glucose-error grid analysis.

OBJECTIVE: To compare the clinical accuracy of two different continuous glucose sensors (CGS) during euglycemia and hypoglycemia using continuous glucose-error grid analysis (CG-EGA). RESEARCH DESIGN AND METHODS: FreeStyle Navigator (Abbott Laboratories, Alameda, CA) and MiniMed CGMS (Medtronic, Northridge, CA) CGSs were applied to the abdomens of 16 type 1 diabetic subjects (age 42 +/- 3 years) 12 h before the initiation of the study. Each system was calibrated according to the manufacturer's recommendations. Each subject underwent a hyperinsulinemic-euglycemic clamp (blood glucose goal 110 mg/dl) for 70-210 min followed by a 1-mg.dl(-1).min(-1) controlled reduction in blood glucose toward a nadir of 40 mg/dl. Arterialized blood glucose was determined every 5 min using a Beckman Glucose Analyzer (Fullerton, CA). CGS glucose recordings were matched to the reference blood glucose with 30-s precision, and rates of glucose change were calculated for 5-min intervals. CG-EGA was used to quantify the clinical accuracy of both systems by estimating combined point and rate accuracy of each system in the euglycemic (70-180 mg/dl) and hypoglycemic (<70 mg/dl) ranges. RESULTS: A total of 1,104 data pairs were recorded in the euglycemic range and 250 data pairs in the hypoglycemic range. Overall correlation between CGS and reference glucose was similar for both systems (Navigator, r = 0.84; CGMS, r = 0.79, NS). During euglycemia, both CGS systems had similar clinical accuracy (Navigator zones A + B, 88.8%; CGMS zones A + B, 89.3%, NS). However, during hypoglycemia, the Navigator was significantly more clinically accurate than the CGMS (zones A + B = 82.4 vs. 61.6%, Navigator and CGMS, respectively, P < 0.0005). CONCLUSIONS: CG-EGA is a helpful tool for evaluating and comparing the clinical accuracy of CGS systems in different blood glucose ranges. CG-EGA provides accuracy details beyond other methods of evaluation, including correlational analysis and the original EGA.

Adult↗
PubMed2004

Evaluating the accuracy of continuous glucose-monitoring sensors: continuous glucose-error grid analysis illustrated by TheraSense Freestyle Navigator data.

OBJECTIVE: The objective of this study was to introduce continuous glucose-error grid analysis (CG-EGA) as a method of evaluating the accuracy of continuous glucose-monitoring sensors in terms of both accurate blood glucose (BG) values and accurate direction and rate of BG fluctuations and to illustrate the application of CG-EGA with data from the TheraSense Freestyle Navigator. RESEARCH DESIGN AND METHODS: We approach the design of CG-EGA from the understanding that continuous glucose sensors (CGSs) allow the observation of BG fluctuations as a process in time. We account for specifics of process characterization (location, speed, and direction) and for biological limitations of the observed processes (time lags associated with interstitial sensors). CG-EGA includes two interacting components: 1) point-error grid analysis (P-EGA) evaluates the sensor's accuracy in terms of correct presentation of BG values and 2) rate-error grid analysis (R-EGA) assesses the sensor's ability to capture the direction and rate of BG fluctuations. RESULTS: CG-EGA revealed that the accuracy of the Navigator, measured as a percentage of accurate readings plus benign errors, was significantly different at hypoglycemia (73.5%), euglycemia (99%), and hyperglycemia (95.4%). Failure to detect hypoglycemia was the most common error. The point accuracy of the Navigator was relatively stable over a wide range of BG rates of change, and its rate accuracy decreased significantly at high BG levels. CONCLUSIONS: Traditional self-monitoring of BG device evaluation methods fail to capture the important temporal characteristics of the continuous glucose-monitoring process. CG-EGA addresses this problem, thus providing a comprehensive assessment of sensor accuracy that appears to be a useful adjunct to other CGS performance measures.

Adult↗
PubMed2003

Algorithmic evaluation of metabolic control and risk of severe hypoglycemia in type 1 and type 2 diabetes using self-monitoring blood glucose data.

The optimization of metabolic control in Type 1 and Type 2 diabetes mellitus (T1DM and T2DM, respectively) [i.e., the maintenance of near-normal hemoglobin A(1c) (HbA(1c)) without increasing the risk of hypoglycemia] could be enhanced by analysis of self-monitoring blood glucose (SMBG) data assessing complementary processes: exposure to hyperglycemia and hypoglycemia. We present algorithms that simultaneously estimate HbA(1)c and risk for significant hypoglycemia using 45-60 days of SMBG. The algorithms were developed using a primary data for 96 subjects with T1DM (n = 48) and T2DM, and were validated in an external data for 520 subjects with T1DM (n = 231) and T2DM. All subjects were on insulin. In the primary (external) data the estimation of HbA(1c) had absolute error of 0.5 (0.7) units of HbA(1c) and percent error of 6.8% (8.1%); 96% (96%) of all estimates were within 20% from reference HbA(1c). The SMBG-estimated value of HbA(1c) was closer to current reference HbA(1c) than a reference HbA(1c) value taken only 2-3 months ago. The results in T1DM and T2DM were similar. Linear model predicted future significant hypoglycemia (R(2) = 62%, p < 0.0001). The leading predictor was a previously introduced Low Blood Glucose Index, which alone had R(2) = 55%. Probability model assessed accurately the odds for future moderate/severe hypoglycemia (coefficients of determination 92%/94%). Four risk categories were identified; within moderate- and high-risk category, there was no difference between T1DM and T2DM in the occurrence of prospective significant hypoglycemia. SMBG data allow for accurate estimation of the two most important markers of metabolic control in T1DM and T2DM - HbA(1c) and risk for hypoglycemia.

Adult↗
PubMed2002

Methods for quantifying self-monitoring blood glucose profiles exemplified by an examination of blood glucose patterns in patients with type 1 and type 2 diabetes.

The maintenance of glycemic control in patients with type 1 or type 2 diabetes mellitus (T1DM and T2DM, respectively) is commonly assisted by devices for self-monitoring of blood glucose (SMBG) that store multiple BG determinations. However, besides average BG, no other SMBG characteristics are routinely computed. We describe several SMBG-based measures that quantify the extent and rate of patients' BG excursions into hypoglycemia and hyperglycemia and can be used as markers for patients' vulnerability to hypoglycemia and BG irregularity. These markers are applied to analyze data from patients with T1DM (n = 277) and T2DM (n = 323), all of whom used insulin. T1DM and T2DM patients were matched by HbA(1c), gender, and number of SMBG readings/day. On average, 230 SMBG readings and three HbA(1c) assays were collected for each subject over 3 months. Compared with T2DM, patients with T1DM diabetes had (1) more extreme low and high BGs, (2) greater risk for severe hypoglycemia as quantified by the Low BG Index, (3) faster descent into hypoglycemia as quantified by the risk rate of change/hour, and (4) greater BG irregularity as computed by BG rate of change/hour and BG SD (all p levels < 0.0001). SMBG data allow for computing and frequent updating of various idiosyncratic diabetes characteristics and risk factors. The use of such computations may assist in optimizing patients' glycemic control.

Adult↗
PubMedDate not supplied

The metabolic demands of driving for drivers with type 1 diabetes mellitus.

BACKGROUND: The active cognitive-motor demands of driving may have a significant metabolic demand that could contribute to the development of hypoglycemia. Conversely, symptoms caused by the stress of driving may be confused with hypoglycemia and lead to false alarms. This study examined the metabolic demand and the physiological stress of driving on type 1 diabetes mellitus (T1DM) drivers. METHODS: Forty-three T1DM drivers were placed on a constant insulin infusion/variable dextrose infusion to maintain euglycemia for 30 min while either watching a driving video or actually driving a simulator, in a counterbalanced crossover design. Dextrose infusion, heart rate, epinephrine, and subjective symptom ratings were measured every 5 min. Additionally, subjects were monitored for self-treatment (drinking soda). RESULTS: While blood glucose (BG) levels were equivalent across both conditions, actual driving was associated with a higher dextrose infusion rate (p = 0.02), more autonomic symptoms (p < 0.05), increased heart rate (p < 0.001), a trend (p = 0.09) for greater epinephrine release, and more frequent hypoglycemic self-treatment (p < 0.001). CONCLUSIONS: Driving is a task with a significant metabolic demand, which may lower BG, and also that driving stress may be associated with symptoms similar to those of hypoglycemia. Physicians should discuss with their T1DM patients hypoglycemia and driving, and encourage measuring blood glucose before driving and during long drives.

Adult↗