Continuous Home Monitoring of Glucose

Improved glycemic control with real-life use of continuous glucose sensors in adult subjects with type 1 diabetes

  1. Satish K. Garg, MD123,
  2. William C. Kelly, BS1,
  3. Mary K. Voelmle, MS, FNP, CDE13,
  4. Peter J. Ritchie, BA1,
  5. Peter A. Gottlieb, MD123,
  6. Kim K. McFann, PHD14 and
  7. Samuel L. Ellis, PHARMD, CDE1
  1. 1Barbara Davis Center for Childhood Diabetes, University of Colorado Health Sciences Center, Aurora, Colorado
  2. 2Department of Internal Medicine, University of Colorado at Denver, Aurora, Colorado
  3. 3Department of Pediatrics, University of Colorado at Denver, Aurora, Colorado
  4. 4Department of Preventive Medicine and Biometrics, University of Colorado at Denver, Aurora, Colorado
  1. Address correspondence and reprint requests to Satish K. Garg, MD, Barbara Davis Center for Childhood Diabetes, University of Colorado at Denver, 1775 North Ursula St., Aurora, CO 80045. E-mail: satish.garg{at}uchsc.edu

Improving metabolic control reduces micro- and macrovascular complications of diabetes. However, intensive insulin therapy increases severe hypoglycemia more than threefold (13). Continuous glucose monitoring (CGM) is being introduced into routine clinical care despite a lack of reimbursement. Registration studies for the Food and Drug Administration (FDA) documented that subjects using real-time CGM improve glucose excursions, reduce variability, decrease time spent in hypoglycemia and hyperglycemia, and improve A1C values (49). Despite these reports, there are data unsupportive of new technologies such as CGM (10) or personal digital assistants (11) for reducing hypoglycemia. This study evaluates glucose control and its relationship with glucose target ranges with continuous home monitoring of glucose (CHMG).

RESEARCH DESIGN AND METHODS

Inclusion criteria limited analysis to subjects with A1C values and downloaded CHMG data at baseline and 3 months, as well as software to download receivers (not available for the first 9 months). Patients who were pregnant or planning a pregnancy were excluded.

A total of 24 subjects on CHMG were included in this analysis. All patients in this study used the DexCom STS sensor (DexCoM, San Diego, CA). Subjects were computer matched for baseline A1C (± 0.3%), sex, age, and duration of diabetes except for one subject in the CHMG group, who had diabetes for 57 years. Baseline demographics were similar between groups (Table 1). This protocol was institutional review board approved.

Subjects initiating CHMG attended a session on glucose trends, features of the CHMG receiver, and proper insertion techniques conducted by certified diabetes educators. All subjects were instructed not to change treatment based on their first week of CHMG use.

All subjects had baseline and 12-week A1C measurements (DCA 2000; Bayer, Tarrytown, NY). The CHMG data were downloaded prospectively at baseline and at 6 (± 2) and 12 (± 2) weeks, except for one subject who did not have 6-week data. Subjects wore sensors as they felt necessary. Subjects were taught to override the receiver every 3 days and use the same sensor for an additional 3 days. All subjects in the comparison group received similar diabetes care. No 6-week data or fingerstick SMBG measurements were available for the comparison group.

CHMG data were analyzed for within (WTRs) (60–150 mg/dl), above (ATRs) (>150 mg/dl), and below (BTRs) (<60 mg/dl) target ranges of blood glucose. The BTR of <60 mg/dl was used as a result of clinical observations that subjects using CHMG are more likely to treat glucose values of 60 mg/dl as opposed to 70 mg/dl, which was used for BTR in our previous self-monitoring of blood glucose (SMBG) publication (12). The ATR readings were further analyzed for 151–240 and >240 mg/dl. The percentages of readings within each target range were compared among baseline and 6- and 12-week data. The number of subjects reaching target A1C values was also analyzed. No subject had severe hypoglycemia needing glucagon or emergency room visits.

Statistical analysis

Analyses of A1C change from baseline and time within glycemic ranges were performed using SAS software (version 9.1; SAS, Cary, NC). Two-tailed tests were used unless otherwise stated. Baseline characteristics were compared using independent-samples t tests. Fisher’s exact tests were performed on the number of subjects reaching target A1C values at baseline. Logistic regression, with baseline A1C target as a covariate, was used to examine whether the experimental group was more likely than the comparison group to reach A1C targets by 3 months. Mixed-model repeated-measures analysis was used to evaluate the change over time in A1C, insulin dose, and the number of patients WTR, BTR, and ATR of blood glucose within the CHMG group.

RESULTS

Mean ± SD sensor use per subject was 17.6 ± 8.4 days per month. Subjects extended the use (despite 3-day approval and now FDA approval for 7 days) of sensors to 6.8 ± 1.6 days.

Changes in A1C

A1C values at baseline were 7.43 ± 1.0 and 7.39 ± 1.0% for the CHMG and comparison groups, respectively (P = 0.896) (Table 1). There was a significant decrease in A1C in the CHMG group (0.4 ± 0.5%; P = 0.047, mixed repeated-measures analysis) at 12 weeks, with a nonsignificant increase in A1C (0.3 ± 1.1%; P = 0.0710) in the comparison group. Also, at 12 weeks there was a difference in A1C values between groups (P = 0.0385) despite the fact that there was no change in insulin dose. The number of subjects achieving A1C values <7.5% was higher in the CHMG group at 12 weeks (OR 7.229; P = 0.0234) (13).

Glucose target ranges

Subjects using CHMG increased WTR glucose readings by 6.5 ± 15.0% (P = 0.0353) and reduced mean ATR glucose readings by 5.6 ± 16.7% (P = 0.0355) at 12 weeks compared with baseline. ATR glucose values also showed a significant reduction in readings >240 mg/dl by 6.4 ± 14.0% (P = 0.0351) at 12 weeks. Results were similar for subjects using multiple daily injections or insulin pumps. Pie charts for glucose ranges are available in an online appendix at http://dx.doi.org/10.2337/dc07-1436.

CONCLUSIONS

This study demonstrates that use of real-time CHMG is associated with improved metabolic control over 12 weeks in adults with type 1 diabetes, as previously documented (8,1419). This study supports previous findings carrying over to real-life use of CHMG in subjects with reasonable glucose control (A1C ∼7.4%). The modest improvement in A1C of 0.4% could be due to the subject population, the short-term nature of the study, and near-target baseline A1C values of 7.43%.

Improvements in metabolic control with CHMG were not associated with increased hypoglycemia, supporting earlier findings (8,1419). The mean increase in WTR glucose readings of 6.5% and decrease in ATR glucose readings of 5.6% at 3 months corresponded with a 0.4% decline in A1C, which is lower than was expected based on our previous self-monitoring of blood glucose data (12). This could be due to lower A1C values at baseline.

Limitations of this study include a small sample size, shorter follow-up, and lack of a randomized control group. However, the data show that CHMG use results in a small A1C reduction without increasing hypoglycemia, most likely due to behavioral changes.

We conclude that use of CHMG can further improve glucose control in subjects with relatively well-controlled type 1 diabetes, with no increase in hypoglycemia. Prospective randomized clinical trials using CHMG with a large sample size need to be conducted.

Table 1—

Demographics and results

Acknowledgments

This study was sponsored in part by grant 08 FLA 00250 from the State of Colorado Public Health and Environment; grant P30 DK575616 from the Diabetes Endocrine Research Center, National Institutes of Health (NIH); grant M01 RR00069 from the General Clinical Research Centers Program, NIH; and grants R01 HL61753, RO1 HL079611, and RO1 DK32493 from the Children’s Diabetes Foundation (Denver, CO).

Footnotes

  • Published ahead of print at http://care.diabetesjournals.org on 11 September 2007. DOI: 10.2337/dc07-1436.

    Additional information for this article can be found in an online appendix at http://dx.doi.org/10.2337/dc07-1436.

    A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

    The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C Section 1734 solely to indicate this fact.

    • Accepted August 31, 2007.
    • Received July 24, 2007.

References

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  1. Diabetes Care vol. 30 no. 12 3023-3025
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