Browse the corpus

Continuous glucose monitors (CGMs), which measure interstitial glucose concentrations, are increasingly being used in clinical practice and in clinical research in patients with diabetes. However, the variation in glucose levels measured by CGM in healthy, nondiabetic individuals during daily living has not been extensively studied. The aim of this study was to characterize CGM glucose patterns in healthy, nondiabetic individuals.

in clinical practice and in clinical research in patients with diabetes. However, the variation in glucose levels measured by CGM in healthy, nondiabetic individuals during daily living has not been extensively studied. The aim of this study was to characterize CGM glucose patterns in healthy, nondiabetic individuals. RESEARCH DESIGN AND METHODS The study was conducted at 10 adult and pediatric diabetes centers, after approval by their institutional review boards. Subjects were healthy adults, adolescents, and children who were clinic staff, friends, relatives of clinic staff, or relatives or acquaintances of an individual with type 1 diabetes. Subjects provided written informed consent and children gave assent to study participation. Inclusion criteria were: age ≥8 years old; BMI 10th to 90th percentile for age and sex for subjects <18 years old (based on 2000 Centers for Disease Control and Prevention (CDC) nomogram) and <28 kg/m2 for subjects ≥18 years old; no significant chronic illness or taking of any medications that might affect glucose metabolism; A1C ≤6.0%; fasting blood glucose 70 to 99 mg/dl; 2-h oral glucose tolerance test (OGTT) level ≤140 mg/dl; and negative anti-GAD, anti-IA2, and anti-insulin antibodies. Of 148 subjects screened for the study, 39 were excluded because of low fasting glucose (n = 3), elevated fasting glucose (n = 16), elevated 2-h glucose (n = 5), positive antibodies (n = 8), ineligible BMI (n = 3), ineligible A1C (n = 1), or insufficient sensor data (n = 3).

-GAD, anti-IA2, and anti-insulin antibodies. Of 148 subjects screened for the study, 39 were excluded because of low fasting glucose (n = 3), elevated fasting glucose (n = 16), elevated 2-h glucose (n = 5), positive antibodies (n = 8), ineligible BMI (n = 3), ineligible A1C (n = 1), or insufficient sensor data (n = 3). Subjects used either a Guardian Clinical (n = 38; Medtronic MiniMed, Northridge, CA) for 3 days, a FreeStyle Navigator (n = 36; Abbott Diabetes Care, Alameda, CA) for 5 days, or a DexCom SEVEN (n = 35, DexCom, San Diego, CA) for 7 days. Subjects were instructed on calibration of the devices using a home blood glucose meter. Results using the DexCom sensor were not included in the analysis because of the frequency of missing data because of overnight dropout of sensor function, and because there was a disproportionate number of low and high glucose values compared with the other sensors, which seemed unlikely to represent true extreme values in these nondiabetic individuals. Notably, the current commercially available DexCom device contains newer software than the devices used in our study. The discrepancies between the DexCom results and the other two devices is shown in supplemental Table A-1, available in an online appendix at http://care.diabetesjournals.org/cgi/content/full/dc09-1971/DC1.

bly, the current commercially available DexCom device contains newer software than the devices used in our study. The discrepancies between the DexCom results and the other two devices is shown in supplemental Table A-1, available in an online appendix at http://care.diabetesjournals.org/cgi/content/full/dc09-1971/DC1. Computed statistics included are mean ± SD for the glucose and medians for the percentage of sensor glucose values in glucose ranges and glucose variability measures overall and in four age-groups: 8 to <15, 15 to <25, 25 to <45, and ≥45 years. The association of A1C, fasting blood glucose, and 2-h postprandial blood glucose with age was assessed using least-squares regression models. The associations of mean sensor glucose and glucose variability measures with age were assessed using least-squares regression models adjusting for device type. The repeated-measures regression models were used to compare mean glucose and glucose variability measures during daytime versus nighttime, adjusting for device type and age. Rank scores were transformed to have a normal distribution, using van der Waerden scores for glucose variability measures because of the skewed distributions. Regression models of glucose variability measures were adjusted for mean glucose as a covariate.

ing daytime versus nighttime, adjusting for device type and age. Rank scores were transformed to have a normal distribution, using van der Waerden scores for glucose variability measures because of the skewed distributions. Regression models of glucose variability measures were adjusted for mean glucose as a covariate. RESULTS The 74 subjects ranged in age from 9 to 65 years old. Of them, 51 (69%) were female; 55 (74%) were non-Hispanic, Caucasian; 13 (18%) were Hispanic; 1 (1%) was African American; and 5 (7%) were other race/ethnicities. Mean A1C (± SD) was 5.3 ± 0.3% (range 4.7–6.0%), fasting glucose was 86 ± 8 mg/dl, and 2-h post-OGTT was 96 ± 22 mg/dl; none of which varied meaningfully by age. Median BMI percentile was 82nd (interquartile range 62nd to 91st) for subjects <18 years old (n = 26) and median BMI was 24.9 kg/m2 (23.3, 26.3) for those ≥18 years old (n = 48).

5.3 ± 0.3% (range 4.7–6.0%), fasting glucose was 86 ± 8 mg/dl, and 2-h post-OGTT was 96 ± 22 mg/dl; none of which varied meaningfully by age. Median BMI percentile was 82nd (interquartile range 62nd to 91st) for subjects <18 years old (n = 26) and median BMI was 24.9 kg/m2 (23.3, 26.3) for those ≥18 years old (n = 48). CGM glucose values were obtained for a mean of 84 ± 21 h per subject. As shown in Table 1, the mean sensor glucose concentration was slightly higher during the day (6:00 a.m. to midnight) than during the night (midnight to 6:00 a.m., P < 0.001 comparing day and night). There was a slight association of lower age and higher mean glucose level (P = 0.009), which was seen both during the day (P = 0.04) and overnight (P < 0.001) (Table 1; supplemental Figs. A-1 and A-2). Hourly means ranged from 92 mg/dl from 5:00 to 6:00 a.m. to 103 mg/dl from 8:00 to 10:00 p.m. (supplemental figure A-2). The median percentage of sensor values between 71 and 120 mg/dl was 91%, 0.2% of values being ≤60 mg/dl and 0.4% >140 mg/dl; no subjects had 100% of values between 71 and 120 mg/dl (Table 1; supplemental Tables A-2 and A-3). Except for a slight tendency for a higher rate of change in younger subjects (P = 0.04), other measures of glucose variability were not influenced by age (Table 1). Glucose variability was lower at night than during the day (P < 0.001; Table 1). Results were similar comparing the Navigator and Guardian Clinical CGM devices (mean glucose 98 ± 11 and 98 ± 9 mg/dl, respectively). Table 1 Sensor glucose values and glucose variability by age group and time of day

CGM glucose values were obtained for a mean of 84 ± 21 h per subject. As shown in Table 1, the mean sensor glucose concentration was slightly higher during the day (6:00 a.m. to midnight) than during the night (midnight to 6:00 a.m., P < 0.001 comparing day and night). There was a slight association of lower age and higher mean glucose level (P = 0.009), which was seen both during the day (P = 0.04) and overnight (P < 0.001) (Table 1; supplemental Figs. A-1 and A-2). Hourly means ranged from 92 mg/dl from 5:00 to 6:00 a.m. to 103 mg/dl from 8:00 to 10:00 p.m. (supplemental figure A-2). The median percentage of sensor values between 71 and 120 mg/dl was 91%, 0.2% of values being ≤60 mg/dl and 0.4% >140 mg/dl; no subjects had 100% of values between 71 and 120 mg/dl (Table 1; supplemental Tables A-2 and A-3). Except for a slight tendency for a higher rate of change in younger subjects (P = 0.04), other measures of glucose variability were not influenced by age (Table 1). Glucose variability was lower at night than during the day (P < 0.001; Table 1). Results were similar comparing the Navigator and Guardian Clinical CGM devices (mean glucose 98 ± 11 and 98 ± 9 mg/dl, respectively). Table 1 Sensor glucose values and glucose variability by age group and time of day All Age group Time of day 8–<15 15–<25 25–<45 ≥45 Daytime Nighttime n 74 20 17 20 17 A1C (%) 5.3 ± 0.3 5.3 ± 0.2 5.2 ± 0.2 5.2 ± 0.3 5.5 ± 0.3 Sensor glucose Overall* 98 ± 10 103 ± 11 97 ± 7 96 ± 12 95 ± 7 Daytime† 99 ± 10 103 ± 10 98 ± 7 97 ± 12 97 ± 6 Nighttime‡ 95 ± 13 101 ± 15 97 ± 11 91 ± 12 89 ± 11 Peak sensor glucose§ Daytime 131 134 135 125 128 Nighttime 109 111 111 104 103 Nadir sensor glucose§ Daytime 73 74 71 75 79 Nighttime 80 84 80 77 78 Distribution of sensor glucose levels 71–120 mg/dl 91.0% 85.1% 87.9% 91.4% 93.7% 90.4% 90.3% ≤70 mg/dl 1.7% 1.8% 0.6% 2.9% 1.6% 1.1% 2.2% ≤60 mg/dl 0.2% 0.2% 0.0% 0.2% 0.1% 0.0% 0.0% >120 mg/dl 5.6% 8.2% 8.3% 4.2% 4.4% 5.9% 1.1% >140 mg/dl 0.4% 1.3% 0.3% 0.3% 0.0% 0.5% 0.0% Overall glucose variability SD (mg/dl)‖ 13.7 16.4 13.7 12.6 12.4 13.5 10.9 MARC (mg/dl/min)‖ 0.34 0.36 0.34 0.32 0.33 0.37 0.26 Coefficient of variation (%)‖¶ 14 16 14 14 13 14 12 MAGE (mg/dl)‖ 27.7 28.1 28.3 25.6 26.9 28.0 15.8 Data are means ± SD and medians.

4% 5.9% 1.1% >140 mg/dl 0.4% 1.3% 0.3% 0.3% 0.0% 0.5% 0.0% Overall glucose variability SD (mg/dl)‖ 13.7 16.4 13.7 12.6 12.4 13.5 10.9 MARC (mg/dl/min)‖ 0.34 0.36 0.34 0.32 0.33 0.37 0.26 Coefficient of variation (%)‖¶ 14 16 14 14 13 14 12 MAGE (mg/dl)‖ 27.7 28.1 28.3 25.6 26.9 28.0 15.8 Data are means ± SD and medians. *P = 0.009 for the association of mean glucose with age, adjusting for device type. †P = 0.04 for the association of mean glucose with age, adjusting for device type. ‡P < 0.001 for the association of mean glucose with age, adjusting for device type. §The calculation of peak and nadir glucose was restricted to days with ≥12 h and nights with ≥4 h of glucose data. ‖P < 0.001 for the association of glucose variability with time of day, adjusting for device type and mean glucose. ¶Coefficient of variation = SD/mean glucose. MARC, mean absolute rate of change; MAGE, mean amplitude of glycemic excursions. CONCLUSIONS In this study we have described sensor glucose profiles using the Medtronic and Abbott Diabetes CGM systems in healthy, anti–β-cell antibody–negative subjects across the spectrum of pediatric and adult age ranges. Our mean sensor data were similar to those reported in healthy Chinese subjects using Medtronic's Continuous Glucose Monitoring System (1). However, in that study, sensor values increased with advancing age, contrary to our data, and only subjects >20 years old were included.

spectrum of pediatric and adult age ranges. Our mean sensor data were similar to those reported in healthy Chinese subjects using Medtronic's Continuous Glucose Monitoring System (1). However, in that study, sensor values increased with advancing age, contrary to our data, and only subjects >20 years old were included. Our findings may be useful to clinicians and investigators who are using these devices in patients with abnormal glucose tolerance or diabetes. Our results describe the frequency of out-of-range sensor glucose values and the degree of glucose variability that are likely to occur in normoglycemic individuals. This provides a better understanding of what constitutes biochemical hypo- or hyperglycemia reported by these devices. Supplementary Material Online Appendix The study was designed and conducted by the investigators listed in the appendix, who collectively wrote the manuscript and vouch for the data. The investigators had complete autonomy to analyze and report the trial results. There were no agreements concerning confidentiality of the data between the Juvenile Diabetes Research Foundation, Inc., and the authors or their institutions. The Jaeb Center for Health Research had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. 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.

Supplementary Material Online Appendix The study was designed and conducted by the investigators listed in the appendix, who collectively wrote the manuscript and vouch for the data. The investigators had complete autonomy to analyze and report the trial results. There were no agreements concerning confidentiality of the data between the Juvenile Diabetes Research Foundation, Inc., and the authors or their institutions. The Jaeb Center for Health Research had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. 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. Acknowledgments The study was designed and conducted by the investigators listed in the appendix, who collectively wrote the manuscript and vouch for the data. The investigators had complete autonomy to analyze and report the trial results. There were no agreements concerning confidentiality of the data between the Juvenile Diabetes Research Foundation, Inc., and the authors or their institutions. The Jaeb Center for Health Research had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. For a complete conflict of interest statement, please refer to the online appendix available at http://care.diabetesjournals.org/cgi/content/full/dc09-1974/DC1. No other potential conflicts of interest relevant to this article were reported.

nsibility for the integrity of the data and the accuracy of the data analysis. For a complete conflict of interest statement, please refer to the online appendix available at http://care.diabetesjournals.org/cgi/content/full/dc09-1974/DC1. No other potential conflicts of interest relevant to this article were reported. Parts of this study were presented in poster form at the 69th Scientific Sessions of the American Diabetes Association, New Orleans, Louisiana, 5–9 June 2009. The Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group recognizes the efforts of the subjects and their families and thanks them for their participation. APPENDIX Lead Authors: Larry A. Fox, MD; Roy W. Beck, MD, PhD; Dongyuan Xing, MPH. Additional members of the writing committee (alphabetical): H. Peter Chase, MD; Lisa K. Gilliam, MD, PhD; Irl Hirsch, MD; Craig Kollman, PhD; Lori Laffel, MD, MPH; Joyce Lee, MD; Katrina J. Ruedy, MSPH; William V. Tamborlane, MD; Michael Tansey, MD; and Darrell M. Wilson, MD.

Even with the use of insulin pumps and long-acting insulin analogs, severe hypoglycemia is common in patients with type 1 diabetes, especially during sleep at night. In the Diabetes Control and Complications Trial, more than half of severe hypoglycemic events occurred during sleep (1), and other studies have shown an even greater incidence of severe nocturnal hypoglycemic events in type 1 diabetes (2). Moreover, Sovik and Thordarson (3) reported that among patients aged <40 years who died over a 10-year period, 6% of the deaths were due to “dead-in-bed” syndrome, which in many instances probably was the result of severe nocturnal hypoglycemia. Delayed glucose-lowering effects of afternoon exercise (4), sleep-induced defects in counterregulatory hormone responses to hypoglycemia (5–7), and missed bedtime snacks (8) are among the contributing causes of severe nocturnal hypoglycemic events.

any instances probably was the result of severe nocturnal hypoglycemia. Delayed glucose-lowering effects of afternoon exercise (4), sleep-induced defects in counterregulatory hormone responses to hypoglycemia (5–7), and missed bedtime snacks (8) are among the contributing causes of severe nocturnal hypoglycemic events. Studies that used retrospective and real-time continuous glucose monitoring (CGM) systems to assess glycemic control of type 1 diabetes indicate that severe hypoglycemic events are only the tip of the iceberg regarding the risk of nocturnal hypoglycemia, because many more events are unrecognized and asymptomatic (8–14). Detection of such events is important, however, because recurrent episodes of mild hypoglycemia have been shown to contribute to the development of defective counterregulatory hormone responses to subsequent reductions in blood glucose, thus setting the stage for clinically important hypoglycemic events. Buckingham et al. (15) documented four episodes of seizures occurring during the night in patients wearing CGM devices, which demonstrated that there were 2¼–4 h of low sensor glucose values preceding each seizure.

sequent reductions in blood glucose, thus setting the stage for clinically important hypoglycemic events. Buckingham et al. (15) documented four episodes of seizures occurring during the night in patients wearing CGM devices, which demonstrated that there were 2¼–4 h of low sensor glucose values preceding each seizure. Our Juvenile Diabetes Research Foundation (JDRF) CGM Study Group recently reported the results of a 6-month randomized clinical trial and 6-month extension study that evaluated the effectiveness of real-time CGM in intensively treated type 1 diabetic subjects with baseline A1C levels ≥7.0% (n = 322) and <7.0% (n = 129) (16–18). These studies have provided a very large dataset of nighttime CGM profiles to evaluate the frequency of nocturnal hypoglycemia during 12 months of CGM use in the home environment and factors associated with greater risk.

ated type 1 diabetic subjects with baseline A1C levels ≥7.0% (n = 322) and <7.0% (n = 129) (16–18). These studies have provided a very large dataset of nighttime CGM profiles to evaluate the frequency of nocturnal hypoglycemia during 12 months of CGM use in the home environment and factors associated with greater risk. RESEARCH DESIGN AND METHODS The study protocol and clinical characteristics of enrolled subjects have been described in detail elsewhere (16,17,19). Major eligibility criteria included age ≥8 years, type 1 diabetes for at least 1 year, use of either an insulin pump or multiple (at least three) daily insulin injections, and A1C level <10.0%. The dataset used for the current analyses included 180 subjects assigned to the CGM group who used either the FreeStyle Navigator (Abbott Diabetes Care, Alameda, CA) or the MiniMed Paradigm REAL-Time Insulin Pump and Continuous Glucose Monitoring System (Medtronic MiniMed, Northridge, CA). At baseline, a blinded CGM device was used for 1 week. Thereafter, the goal was to use the unblinded CGM device on a daily basis if possible. CGM glucose data were downloaded at each visit over 12 months of follow-up. Subjects and parents of minor subjects completed the Hypoglycemia Fear Survey (20) at baseline, 6 months, and 12 months.

nded CGM device was used for 1 week. Thereafter, the goal was to use the unblinded CGM device on a daily basis if possible. CGM glucose data were downloaded at each visit over 12 months of follow-up. Subjects and parents of minor subjects completed the Hypoglycemia Fear Survey (20) at baseline, 6 months, and 12 months. The CGM data were evaluated from midnight to 6:00 a.m. Only nights having at least 4 h of glucose data were included in the analysis. Subjects needed to have at least 42 such nights to be included in the analysis (this restriction was placed because hypoglycemia rates were calculated per subject). Four subjects did not meet this criterion and were not included in the analysis. The dataset included 36,467 nights from 176 subjects with a median value of 217 nights per subject. Of the nights, 86% had the full 6 h of data without any skips from midnight to 6:00 a.m. A hypoglycemia event was defined as the occurrence of at least two CGM glucose values ≤60 mg/dl within a 20-min period. The percentage of nights with at least one hypoglycemia event was computed for each subject.

ights per subject. Of the nights, 86% had the full 6 h of data without any skips from midnight to 6:00 a.m. A hypoglycemia event was defined as the occurrence of at least two CGM glucose values ≤60 mg/dl within a 20-min period. The percentage of nights with at least one hypoglycemia event was computed for each subject. The associations between nocturnal hypoglycemia rate, defined as the percentage of nights with hypoglycemia per subject, and baseline demographic and clinical factors (listed in Table 1) were evaluated using regression models. Because of the skewed distribution of the hypoglycemia rate, a rank transformation (van der Waerden scores) was used in the models. Baseline demographic and clinical factors with P < 0.20 in the univariate model were included in an initial multivariate model and then a backward elimination procedure was used to remove variables with P > 0.05. A forward selection process resulted in a similar model. Age was evaluated as a discrete factor in three prespecified levels (8–14, 15–24, and ≥25 years). To avoid collinearity in the model building, the highly correlated baseline hypoglycemic measures (percentage of daytime, nighttime, or 24 h with hypoglycemia and number of nights with hypoglycemia) and other baseline glycemic measures (the percentage of blinded CGM values between 71 and 180 mg/dl, the percentage of values >250 mg/dl, and A1C) were included in the model one at a time. Subjects with missing values for covariates were excluded from the corresponding univariate models. For the multivariate models, missing was treated as a separate category for discrete covariates and an indicator for missing was added to the model for continuous covariates. The association of age and hypoglycemia duration during nights with a hypoglycemic event was evaluated using repeated-measures regression with rank scores. The comparison of the hypoglycemia rate in the first 6 months and in the second 6 months was based on rank scores.

was added to the model for continuous covariates. The association of age and hypoglycemia duration during nights with a hypoglycemic event was evaluated using repeated-measures regression with rank scores. The comparison of the hypoglycemia rate in the first 6 months and in the second 6 months was based on rank scores. Table 1 Baseline characteristics Overall Age-group 8–14 years 15–24 years ≥25 years n 176 64 42 70 Age (years) 25.6 ± 15.6 11.6 ± 2.0 19.6 ± 3.2 42.1 ± 11.4 Diabetes duration (years) 14.7 ± 12.5 6.1 ± 3.1 10.2 ± 5.1 25.4 ± 13.2 Sex Female 94 (53) 34 (53) 21 (50) 39 (56) Male 82 (47) 30 (47) 21 (50) 31 (44) Severe hypoglycemia events in 6 months before study (self-reported) 0 164 (93) 61 (95) 39 (93) 64 (91) ≥1 12 (7) 3 (5) 3 (7) 6 (9) Nights with hypoglycemia during blinded use at baseline* 0 102 (60) 42 (67) 21 (51) 39 (59) ≥1 68 (40) 21 (33) 20 (49) 27 (41) Home blood glucose meter measurements per day (self-reported at baseline)† 6.8 ± 2.3 6.8 ± 2.0 6.0 ± 2.1 7.1 ± 2.5 ≤5 43 (29) 12 (23) 16 (52) 15 (23) 6–8 78 (53) 31 (60) 12 (39) 35 (55) >8 26 (18) 9 (17) 3 (10) 14 (22) Insulin delivery Pump 163 (93) 57 (89) 38 (90) 68 (97) Multiple daily injections 13 (7) 7 (11) 4 (10) 2 (3) A1C 7.4 ± 0.9 7.6 ± 1.0 7.6 ± 0.8 7.1 ± 0.8 <7.0% 57 (32) 17 (27) 11 (26) 29 (41) 7.0–<8.0% 72 (41) 22 (34) 16 (38) 34 (49) ≥8.0% 47 (27) 25 (39) 15 (36) 7 (10) Hypoglycemia Fear Scale score‡ 28 ± 18 25 ± 17 29 ± 18 31 ± 18 <20 65 (37) 27 (42) 15 (36) 23 (33) 20–<30 32 (18) 14 (22) 8 (19) 10 (14) ≥30 78 (45) 22 (35) 19 (45) 37 (53) Data are means ± SD or n (%).

<7.0% 57 (32) 17 (27) 11 (26) 29 (41) 7.0–<8.0% 72 (41) 22 (34) 16 (38) 34 (49) ≥8.0% 47 (27) 25 (39) 15 (36) 7 (10) Hypoglycemia Fear Scale score‡ 28 ± 18 25 ± 17 29 ± 18 31 ± 18 <20 65 (37) 27 (42) 15 (36) 23 (33) 20–<30 32 (18) 14 (22) 8 (19) 10 (14) ≥30 78 (45) 22 (35) 19 (45) 37 (53) Data are means ± SD or n (%). *From use of a blinded CGM device for 1 week at baseline, missing for 6 subjects. †Collected on randomization form, as assessed by clinic personnel over the last 7 days. A question was added to Case Report Form after study initialization, and data were missing for 29 subjects. ‡The Hypoglycemia Fear Scale consists of 15 5-point Likert scale items, with scores scaled to a 0–100 range with higher scores indicating more fear of hypoglycemia; missing for 1 subject. Analyses were conducted using SAS (version 9.1, SAS Institute, Cary, NC). All P values are two-sided. Because of the exploratory nature of these analyses and the multiple statistical tests, the threshold for statistical significance was adjusted to P < 0.01.

‡The Hypoglycemia Fear Scale consists of 15 5-point Likert scale items, with scores scaled to a 0–100 range with higher scores indicating more fear of hypoglycemia; missing for 1 subject. Analyses were conducted using SAS (version 9.1, SAS Institute, Cary, NC). All P values are two-sided. Because of the exploratory nature of these analyses and the multiple statistical tests, the threshold for statistical significance was adjusted to P < 0.01. RESULTS The clinical characteristics of the 176 subjects who met the criteria for inclusion in these analyses are shown in Table 1. Hypoglycemic events occurred between midnight and 6:00 a.m. during 3,083 (8.5%) of the 36,467 nights, with the median percentage of nights with hypoglycemia per subject being 7.4% (interquartile range 3.7–12.1%), which is approximately twice per month. The maximum percentage of hypoglycemic nights per subject was 27.8%; six (3%) of subjects had no hypoglycemic nights (number of nights for these subjects ranged from 55 to 235, their baseline A1C ranged from 7.7 to 8.9%) (supplementary Table 1, available in an online appendix at http://care.diabetesjournals.org/cgi/content/full/dc09-2081/DC1).

hypoglycemic nights per subject was 27.8%; six (3%) of subjects had no hypoglycemic nights (number of nights for these subjects ranged from 55 to 235, their baseline A1C ranged from 7.7 to 8.9%) (supplementary Table 1, available in an online appendix at http://care.diabetesjournals.org/cgi/content/full/dc09-2081/DC1). On the 3,083 nights during which hypoglycemia occurred, the median duration of hypoglycemia (≤60 mg/dl) was 53 min (interquartile range 29–110 min) and the mean was 81 ± 75 min, with 47% of nights having at least 1 h of hypoglycemia, 23% at least 2 h, and 11% at least 3 h. An exploratory plot of the duration of hypoglycemia versus age suggested a shorter mean duration of the events in subjects aged ≥25 years old than in those aged <25 years old (Fig. 1). In a statistical comparison of these two age-groups, mean duration of hypoglycemia during the nights on which hypoglycemia occurred was 73 min in subjects aged ≥25 years and 88 min in subjects aged <25 years (median 50 vs. 58 min, P = 0.007). Figure 1 Duration of hypoglycemia (≤60 mg/dl) vs. age. For presentation purposes, the hypoglycemic nights ordered by age were divided into 20 groups with an approximately equal number of nights per group. The average duration was then plotted against the average age for each group. The regression line, however, is based on all the data points, not the 20 groups.

) vs. age. For presentation purposes, the hypoglycemic nights ordered by age were divided into 20 groups with an approximately equal number of nights per group. The average duration was then plotted against the average age for each group. The regression line, however, is based on all the data points, not the 20 groups. As shown in Table 2, a higher incidence of nocturnal hypoglycemia over the 12 months of follow up was associated with 1) lower baseline A1C levels (P < 0.001) and 2) the occurrence of hypoglycemia on one or more nights during baseline blinded CGM use (P < 0.001) in a multivariate model. Similar results were obtained when the percentage of daytime, nighttime, or 24 h with hypoglycemia during the baseline blinded CGM use was included in the model instead of the number of nights with hypoglycemia and when the percentage of blinded CGM values between 71 and 180 mg/dl or the percentage of values >250 mg/dl was included in the model instead of A1C (supplementary Table 2). Table 2 Association of baseline factors and nocturnal hypoglycemia

As shown in Table 2, a higher incidence of nocturnal hypoglycemia over the 12 months of follow up was associated with 1) lower baseline A1C levels (P < 0.001) and 2) the occurrence of hypoglycemia on one or more nights during baseline blinded CGM use (P < 0.001) in a multivariate model. Similar results were obtained when the percentage of daytime, nighttime, or 24 h with hypoglycemia during the baseline blinded CGM use was included in the model instead of the number of nights with hypoglycemia and when the percentage of blinded CGM values between 71 and 180 mg/dl or the percentage of values >250 mg/dl was included in the model instead of A1C (supplementary Table 2). Table 2 Association of baseline factors and nocturnal hypoglycemia n % Nights with hypoglycemia per subject Unadjusted P value Model 1* Model 2† Total 176 7.4 (3.7, 12.1) Age 0.05 0.12 8–14 years 64 6.3 (2.0, 11.4) 15–24 years 42 8.8 (3.9, 16.1) ≥25 years 70 7.4 (4.6, 10.8) Sex 94 7.2 (3.7, 10.8) Female 0.36 Male 82 7.8 (3.7, 14.2) Severe hypoglycemia events in 6 months before to study (self-reported) 0.87 0 164 7.2 (3.7, 12.2) ≥1 12 8.3 (4.3, 10.5) Nights with hypoglycemia during blinded use at baseline‡ <0.001 <0.001 <0.001 0 102 6.0 (2.8, 10.5) ≥1 68 9.4 (5.1, 15.9) Home blood glucose meter measurements per day (self-reported at baseline)§‖ 0.28 ≤5 43 8.1 (4.1, 13.7) 6–8 78 8.8 (3.7, 12.2) >8 26 5.4 (3.2, 12.4) Insulin delivery Pump 163 7.4 (3.9, 12.0) 0.63 Multiple daily injections 13 5.1 (1.8, 12.6) A1C§ <0.001 <0.001 <0.001 <7.0% 57 9.0 (5.3, 14.7) 7.0–<8.0% 72 8.2 (4.5, 12.0) ≥8.0% 47 3.9 (1.6, 8.7) Hypoglycemia Fear Scale score§¶ 0.11 0.22 <20 65 7.5 (3.3, 10.3) 20–<30 32 7.7 (4.6, 11.0) ≥30 78 7.0 (3.7, 13.5) Data are median (25th, 75th percentile).

(3.9, 12.0) 0.63 Multiple daily injections 13 5.1 (1.8, 12.6) A1C§ <0.001 <0.001 <0.001 <7.0% 57 9.0 (5.3, 14.7) 7.0–<8.0% 72 8.2 (4.5, 12.0) ≥8.0% 47 3.9 (1.6, 8.7) Hypoglycemia Fear Scale score§¶ 0.11 0.22 <20 65 7.5 (3.3, 10.3) 20–<30 32 7.7 (4.6, 11.0) ≥30 78 7.0 (3.7, 13.5) Data are median (25th, 75th percentile). *The multivariate regression model included all variables with P < 0.20. †Multivariate regression model using backward selection keeping those variables with P < 0.05. ‡From use of a blinded CGM device for 1 week at baseline, missing for 6 subjects. §P value obtained by treating as continuous variable. ‖Collected on a randomization form, as assessed by clinic personnel over the last 7 days. A question was added to Case Report Form after study initialization, and data were missing for 29 subjects. ¶The Hypoglycemia Fear Scale consists of 15 5-point Likert scale items, with scores scaled to a 0 to 100 range with higher scores indicating more fear of hypoglycemia; missing for 1 subject.

‖Collected on a randomization form, as assessed by clinic personnel over the last 7 days. A question was added to Case Report Form after study initialization, and data were missing for 29 subjects. ¶The Hypoglycemia Fear Scale consists of 15 5-point Likert scale items, with scores scaled to a 0 to 100 range with higher scores indicating more fear of hypoglycemia; missing for 1 subject. There was a suggestion of an upside down U-shaped association between age and hypoglycemia rate. The median hypoglycemia rate was 6.3% in the 8- to 14-year age-group, 8.8% in the 15- to 24-year age-group, and 7.4% in the ≥25-year age-group (univariate P = 0.05, multivariate P = 0.12). The frequency of nocturnal hypoglycemia was not statistically different between pump and multiple daily injection users (P = 0.63). Scores on the Hypoglycemia Fear Survey completed at baseline also were not predictive of the frequency of nocturnal hypoglycemia. The factors associated with hypoglycemia appeared to be similar in the three age-groups (supplementary Table 2). The median hypoglycemia rate was 6.6% (25th and 75th interquartile range 3.5, 12.6%) in the first 6 months and 7.7% (3.7, 13.6%) in the second 6 months (P = 0.45).

of the frequency of nocturnal hypoglycemia. The factors associated with hypoglycemia appeared to be similar in the three age-groups (supplementary Table 2). The median hypoglycemia rate was 6.6% (25th and 75th interquartile range 3.5, 12.6%) in the first 6 months and 7.7% (3.7, 13.6%) in the second 6 months (P = 0.45). CONCLUSIONS The >36,000 nights with ≥4 h of sensor glucose readings, totaling >2.4 million individual glucose values in 176 patients with type 1 diabetes, aged 8–72 years, provided us with a unique opportunity to determine the frequency of nocturnal hypoglycemia. During treatment aimed to lower A1C levels to ≤7.0%, as has been suggested in other smaller studies, the occurrence of nocturnal hypoglycemia in our intensively treated subjects was both frequent, occurring on 8.5% of nights during the 12 months of CGM use, and prolonged. On 23% of hypoglycemic nights, sensor glucose levels ≤60 mg/dl were present for almost 2 h and the duration of hypoglycemia was longer in those aged <25 years. It seems unlikely that the observed incidence of nocturnal hypoglycemia is an overestimate because prior outpatient studies using CGM have reported even higher rates (8,9,11–13), as have inpatient studies using blood glucose measurements (10,14). Although sensor inaccuracy could produce misclassification of some nights as to whether hypoglycemia occurred, an inpatient accuracy study conducted by the Diabetes Research in Children Network using the FreeStyle Navigator showed that the false-positive and false-negative rates for nocturnal hypoglycemia were approximately the same (21). Thus, the point estimate of nocturnal hypoglycemia from the current study is unlikely to be appreciably affected by sensor inaccuracy.

Diabetes Research in Children Network using the FreeStyle Navigator showed that the false-positive and false-negative rates for nocturnal hypoglycemia were approximately the same (21). Thus, the point estimate of nocturnal hypoglycemia from the current study is unlikely to be appreciably affected by sensor inaccuracy. A sensor glucose level ≤60 mg/dl rather than ≤70 mg/dl was used to define hypoglycemia because there is considerably greater concern for serious sequelae for glucose levels ≤60 mg/dl than for levels between 61 and 70 mg/dl. Moreover, in our study of sensor glucose levels in 8- to 65-year-old, healthy, nonobese subjects with normal fasting glucose and normal glucose tolerance, nighttime sensor glucose values ≤60 mg/dl were much less common than values between 61 and 70 mg/dl (median frequency 0.0 vs. 1.0%, respectively, P < 0.001) (22). Not surprisingly, the frequency of nighttime hypoglycemia was greater in subjects with lower A1C values and in those who had the occurrence of nocturnal hypoglycemia during a week of blinded CGM use at baseline. The method of insulin administration was not a significant predictor, but the number of patients using multiple daily injections was small, limiting the interpretation of this finding. It also is important to note that nocturnal hypoglycemia was frequent and prolonged in our subjects even though nighttime CGM profiles were being used to adjust overnight basal rates, and long-acting insulin analog doses and sensor alarms were used to limit the duration of nocturnal hypoglycemic events.

f this finding. It also is important to note that nocturnal hypoglycemia was frequent and prolonged in our subjects even though nighttime CGM profiles were being used to adjust overnight basal rates, and long-acting insulin analog doses and sensor alarms were used to limit the duration of nocturnal hypoglycemic events. These results support the contention that overnight insulin replacement may never be optimal in patients with type 1 diabetes until closed-loop systems that provide minute-to-minute feedback control of insulin delivery based on real-time sensor glucose sensor data are developed for home use. Supplementary Material Online Appendix Clinical trial reg. no. NCT00406133, clinicaltrials.gov. 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. Acknowledgments The writing committee members are as follows: Lead authors: Nelly Mauras, MD; Dongyuan Xing, MPH; Roy W. Beck, MD, PhD; and William V. Tamborlane, MD. Additional members (alphabetical): Rosanna Fiallo-Scharer, MD; Irl Hirsch, MD; Craig Kollman, PhD; Lori Laffel, MD, MPH; Joyce Lee, MD, MPH; Katrina J. Ruedy, MSPH; Eva Tsalikian, MD; and Darrell Wilson, MD.

ollows: Lead authors: Nelly Mauras, MD; Dongyuan Xing, MPH; Roy W. Beck, MD, PhD; and William V. Tamborlane, MD. Additional members (alphabetical): Rosanna Fiallo-Scharer, MD; Irl Hirsch, MD; Craig Kollman, PhD; Lori Laffel, MD, MPH; Joyce Lee, MD, MPH; Katrina J. Ruedy, MSPH; Eva Tsalikian, MD; and Darrell Wilson, MD. Funding for this study was provided by the Juvenile Diabetes Research Foundation (grants 22-2006-1107, 22-2006-1117, 22-2006-1112, 22-2006-1123, and 01-2006-8031). Continuous glucose monitors and sensors were purchased at a bulk discount price from DexCom (San Diego, CA), Medtronic MiniMed (Northridge, CA), and Abbott Diabetes Care (Alameda, CA). Home glucose meters and test strips were provided to the study by LifeScan and Abbott Diabetes Care. A listing of relationships of the investigators with companies that make products relevant to the manuscript between 1 July 2006 and 4 November 2009 follows. Research funds listed below were provided to the legal entity that employs the individual and not directly to the individual. C.K. received consulting fees from Medtronic MiniMed. L.L. received consulting fees from LifeScan, consulting fees and speaker honorarium from Abbott Diabetes Care, and consulting fees and research funding from Medtronic MiniMed. N.M. received grant support from Medtronic MiniMed. W.V.T. received consulting fees from Abbott Diabetes Care and LifeScan and consulting fees, speaker honorarium, and research funding from Medtronic MiniMed. No other potential conflicts of interest relevant to this article were reported.

unding from Medtronic MiniMed. N.M. received grant support from Medtronic MiniMed. W.V.T. received consulting fees from Abbott Diabetes Care and LifeScan and consulting fees, speaker honorarium, and research funding from Medtronic MiniMed. No other potential conflicts of interest relevant to this article were reported. The study was designed and conducted by the investigators listed in the online appendix, who collectively wrote the manuscript and vouch for the data. The investigators had complete autonomy to analyze and report the trial results. There were no agreements concerning confidentiality of the data between the Juvenile Diabetes Research Foundation and the authors or their institutions. The Jaeb Center for Health Research had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Parts of this study were presented at the Diabetes Technology Society Meeting, San Francisco, California, 5–7 November 2009. The Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group recognizes the efforts of the subjects and their families and thanks them for their participation.