Abstract
Context
Glycemic control in adolescents with type 1 diabetes is poor; yet, it typically improves during early adulthood. Factors related to improvement of glycemic control are unclear.
...Objective
This work examines how demographic and clinical variables may affect trajectories of glycemic control over time.
Methods
This retrospective, observational study comprised 1775 participants ages 18 to 30 years at enrollment in the T1D Exchange clinic registry. Latent class trajectory modeling was used to determine subgroups following a similar glycated hemoglobin A1c (HbA1c) trajectory over time.
Results
Five distinct trajectories of HbA1c classes were identified: “low-decline” and “moderate-decline” groups had low or moderate HbA1c with a gradual decline, the “high-stable” group had high HbA1c and remained stable, and the “very high-rapid decline” and “very high-slow decline” groups had very high HbA1c with rapid or gradual decline. Compared with the “high-stable” group, the “low-decline” and “moderate-decline” groups were more likely to be male (P = .009), White non-Hispanic (P = .02), nonsmokers (P < .001), check self-monitoring blood glucose (SMBG) more frequently (P < .001), and have higher education (P < .001), lower body mass index (P = .02), and lower daily insulin dose (P < .001). Compared with the “very high-rapid decline” and “very high-slow decline” groups, the “low-decline” and “moderate-decline” groups were more likely to be male (P = .02), have higher education (P < .001), use insulin pumps (P = .01), be nonsmokers (P < .001), and have a higher number of SMBG checks per day at enrollment (P < .001).
Conclusion
We determined 5 distinct patterns of glycemic control from early adulthood into adulthood. Further evaluation into the modifiable factors associated with a declining HbA1c trajectory would aid in the development of targeted interventions.
Overnight hypoglycemia occurs frequently in individuals with type 1 diabetes and can result in loss of consciousness, seizure, or even death. We conducted an in-home randomized trial to determine ...whether nocturnal hypoglycemia could be safely reduced by temporarily suspending pump insulin delivery when hypoglycemia was predicted by an algorithm based on continuous glucose monitoring (CGM) glucose levels.
Following an initial run-in phase, a 42-night trial was conducted in 45 individuals aged 15-45 years with type 1 diabetes in which each night was assigned randomly to either having the predictive low-glucose suspend system active (intervention night) or inactive (control night). The primary outcome was the proportion of nights in which ≥1 CGM glucose values ≤60 mg/dL occurred.
Overnight hypoglycemia with at least one CGM value ≤60 mg/dL occurred on 196 of 942 (21%) intervention nights versus 322 of 970 (33%) control nights (odds ratio 0.52 95% CI 0.43-0.64; P < 0.001). Median hypoglycemia area under the curve was reduced by 81%, and hypoglycemia lasting >2 h was reduced by 74%. Overnight sensor glucose was >180 mg/dL during 57% of control nights and 59% of intervention nights (P = 0.17), while morning blood glucose was >180 mg/dL following 21% and 27% of nights, respectively (P < 0.001), and >250 mg/dL following 6% and 6%, respectively. Morning ketosis was present <1% of the time in each arm.
Use of a nocturnal low-glucose suspend system can substantially reduce overnight hypoglycemia without an increase in morning ketosis.
Multivariate survival trees require few statistical assumptions, are easy to interpret, and provide meaningful diagnosis and prediction rules. Trees can handle a large number of predictors with mixed ...types and do not require predictor variable transformation or selection. These are useful features in many application fields and are often required in the current era of big data. The aim of this article is to introduce the R package MST. This package constructs multivariate survival trees using marginal model and frailty model based approaches. It allows the user to control and see how the trees are constructed. The package can also simulate high-dimensional, multivariate survival data from marginal and frailty models.
The bionic pancreas (BP) is initialized with body weight only and doses insulin autonomously without carbohydrate counting, instead using qualitative meal announcements. In case of device ...malfunction, the BP generates and continuously updates backup insulin doses for injection or pump users, including long-acting insulin dose, a four-period basal insulin profile, short-acting meal doses, and a glucose correction factor. Following a 13-week trial in type 1 diabetes, participants using the BP (6-83 years) completed 2-4 days, in which they were randomly assigned to their prestudy insulin regimen (
= 147) or to follow BP-provided guidance (
= 148). Glycemic outcomes with BP guidance were similar to those reinstituting their prestudy insulin regimen, with both groups having higher mean glucose and lower time-in-range than while using the BP during the 13-week trial. In conclusion, a backup insulin regimen automatically generated by the BP can be safely implemented if need arises to discontinue use of the BP. Clinical Trial Registry:
linicaltrials.gov; NCT04200313.
We explored the association between macronutrient intake and postprandial glucose variability in a large sample of youth living with T1D and consuming free-living meals. In the Type 1 Diabetes ...Exercise Initiative Pediatric (T1DEXIP) Study, youth took photographs before and after their meals on 3 days during a 10 day observation period. We used the remote food photograph method to obtain the macronutrient content of youth's meals. We also collected physical activity, continuous glucose monitoring, and insulin use data. We measured glycemic variability using standard deviation (SD) and coefficient of variation (CV) of glucose for up to 3 h after meals. Our sample included 208 youth with T1D (mean age: 14 ± 2 years, mean HbA1c: 54 ± 14.2 mmol/mol 7.1 ± 1.3%; 40% female). We observed greater postprandial glycemic variability (SD and CV) following meals with more carbohydrates. In contrast, we observed less postprandial variability following meals with more fat (SD and CV) and protein (SD only) after adjusting for carbohydrates. Insulin modality, exercise after meals, and exercise intensity did not influence associations between macronutrients and postprandial glycemic variability. To reduce postprandial glycemic variability in youth with T1D, clinicians should encourage diversified macronutrient meal content, with a goal to approximate dietary guidelines for suggested carbohydrate intake.
Background:Regular physical activity and exercise are fundamental components of a healthy lifestyle for youth living with type 1 diabetes (T1D). Yet, few youth living with T1D achieve the daily ...minimum recommended levels of physical activity. For all youth, regardless of their disease status, minutes of physical activity compete with other daily activities, including digital gaming. There is an emerging area of research exploring whether digital games could be displacing other physical activities and exercise among youth, though, to date, no studies have examined this question in the context of youth living with T1D.Objective:We examined characteristics of digital gaming versus nondigital gaming (other exercise) sessions and whether youth with T1D who play digital games (gamers) engaged in less other exercise than youth who do not (nongamers), using data from the Type 1 Diabetes Exercise Initiative Pediatric study.Methods:During a 10-day observation period, youth self-reported exercise sessions, digital gaming sessions, and insulin use. We also collected data from activity wearables, continuous glucose monitors, and insulin pumps (if available).Results:The sample included 251 youths with T1D (age: mean 14, SD 2 y; self-reported glycated hemoglobin A1c level: mean 7.1%, SD 1.3%), of whom 105 (41.8%) were female. Youth logged 123 digital gaming sessions and 3658 other exercise (nondigital gaming) sessions during the 10-day observation period. Digital gaming sessions lasted longer, and youth had less changes in glucose and lower mean heart rates during these sessions than during other exercise sessions. Youth described a greater percentage of digital gaming sessions as low intensity (82/123, 66.7%) when compared to other exercise sessions (1104/3658, 30.2%). We had 31 youths with T1D who reported at least 1 digital gaming session (gamers) and 220 youths who reported no digital gaming (nongamers). Notably, gamers engaged in a mean of 86 (SD 43) minutes of other exercise per day, which was similar to the minutes of other exercise per day reported by nongamers (mean 80, SD 47 min).Conclusions:Digital gaming sessions were longer in duration, and youth had less changes in glucose and lower mean heart rates during these sessions when compared to other exercise sessions. Nevertheless, gamers reported similar levels of other exercise per day as nongamers, suggesting that digital gaming may not fully displace other exercise among youth with T1D.
Hypoglycemia remains an impediment to good glycemic control, with nocturnal hypoglycemia being particularly dangerous. Information on major contributors to nocturnal hypoglycemia remains critical for ...understanding and mitigating risk.
Continuous glucose monitoring (CGM) data for 855 nights were studied, generated by 45 subjects 15-45 years of age with hemoglobin A1c (HbA1c) levels of ≤8.0% who participated in a larger randomized study. Factors assessed for potential association with nocturnal hypoglycemia (CGM measurement of <60 mg/dL for ≥30 min) included bedtime blood glucose (BG), exercise intensity, bedtime snack, insulin on board, day of the week, previous daytime hypoglycemia, age, gender, HbA1c level, diabetes duration, daily basal insulin, and daily insulin dose.
Hypoglycemia occurred during 221 of 885 (25%) nights and was more frequent with younger age (P<0.001), lower HbA1c levels (P=0.006), medium/high-intensity exercise during the preceding day (P=0.003), and the occurrence of antecedent daytime hypoglycemia (P=0.001). There was a trend for lower bedtime BG levels to be associated with more frequent nocturnal hypoglycemia (P=0.10). Bedtime snack, before bedtime insulin bolus, weekend versus weekday, gender, and daily basal and bolus insulin were not associated with nocturnal hypoglycemia.
Awareness that HbA1c level, exercise, bedtime BG level, and daytime hypoglycemia are all modifiable factors associated with nocturnal hypoglycemia may help patients and providers decrease the risk of hypoglycemia at night. Risk for nocturnal hypoglycemia increased in a linear fashion across the range of variables, with no clear-cut thresholds to guide clinicians or patients for any particular night.
Background:
We developed a system to suspend insulin pump delivery overnight when the glucose trend predicts hypoglycemia. This predictive low-glucose suspend (PLGS) system substantially reduces ...nocturnal hypoglycemia without an increase in morning ketosis. Evaluation of hypoglycemia risk factors that could potentially influence the efficacy of the system remains critical for understanding possible problems with the system and identifying patients that may have the greatest benefit when using the system.
Methods:
The at-home randomized trial consisted of 127 study participants with hemoglobin A1c (A1C) of ≤8.5% (mmol/mol) for patients aged 4-14 years and ≤8.0% for patient aged 15-45 years. Factors assessed included age, gender, A1C, diabetes duration, daily percentage basal insulin, total daily dose of insulin (units/kg-day), bedtime BG, bedtime snack, insulin on board, continuous glucose monitor (CGM) rate of change (ROC), day of the week, time system activated, daytime exercise intensity, and daytime CGM-measured hypoglycemia.
Results:
The PLGS system was effective in preventing hypoglycemia for each factor subgroup. There was no evidence that the PLGS system was more or less effective in preventing hypoglycemia in any one subgroup compared with the other subgroups based on that factor. In addition, the effect of the system on overnight hyperglycemia did not differ in subgroups.
Conclusions:
The PLGS system tested in this study effectively reduced hypoglycemia without a meaningful increase in hyperglycemia across a variety of factors.
IntroductionTo characterize glucose levels during uncomplicated pregnancies, defined as pregnancy with a hemoglobin A1c <5.7% (<39 mmol/mol) in early pregnancy, and without a ...large-for-gestational-age birth, hypertensive disorders of pregnancy, or gestational diabetes mellitus (ie, abnormal oral glucose tolerance test).Research design and methodsTwo sites enrolled 937 pregnant individuals aged 18 years and older prior to reaching 17 gestational weeks; 413 had an uncomplicated pregnancy (mean±SD body mass index (BMI) of 25.3±5.0 kg/m2) and wore Dexcom G6 continuous glucose monitoring (CGM) devices throughout the observed gestational period. Mealtimes were voluntarily recorded. Glycemic levels during gestation were characterized using CGM-measured glycemic metrics.ResultsParticipants wore CGM for a median of 123 days each. Glucose levels were nearly stable throughout all three trimesters in uncomplicated pregnancies. Overall mean±SD glucose during gestation was 98±7 mg/dL (5.4±0.4 mmol/L), median per cent time 63–120 mg/dL (3.5–6.7 mmol/L) was 86% (IQR: 82–89%), median per cent time <63 mg/dL (3.5 mmol/L) was 1.8%, median per cent time >120 mg/dL (6.7 mmol/L) was 11%, and median per cent time >140 mg/dL (7.8 mmol/L) was 2.5%. Mean post-prandial peak glucose was 126±22 mg/dL (7.0±1.2 mmol/L), and mean post-prandial glycemic excursion was 36±22 mg/dL (2.0±1.2 mmol/L). Higher mean glucose levels were low to moderately associated with pregnant individuals with higher BMIs (103±6 mg/dL (5.7±0.3 mmol/L) for BMI ≥30.0 kg/m2 vs 96±7 mg/dL (5.3±0.4 mmol/L) for BMI 18.5–<25 kg/m2, r=0.35).ConclusionsMean glucose levels and time 63–120 mg/dL (3.5–6.7 mmol/L) remained nearly stable throughout pregnancy and values above 140 mg/dL (7.8 mmol/L) were rare. Mean glucose levels in pregnancy trend higher as BMI increases into the overweight/obesity range. The glycemic metrics reported during uncomplicated pregnancies represent treatment targets for pregnant individuals.
Introduction
Continuous glucose monitoring (CGM) can guide treatment for people with type 1 (T1D) and type 2 diabetes (T2D). The ANSHIN study assessed the impact of non‐adjunctive CGM use in adults ...with diabetes using intensive insulin therapy (IIT).
Materials and Methods
This single‐arm, prospective, interventional study enrolled adults with T1D or T2D who had not used CGM in the prior 6 months. Participants wore blinded CGMs (Dexcom G6) during a 20‐day run‐in phase, with treatment based on fingerstick glucose values, followed by a 16‐week intervention phase and then a randomized 12‐week extension phase with treatment based on CGM values. The primary outcome was change in HbA1c. Secondary outcomes were CGM metrics. Safety endpoints were the number of severe hypoglycaemic (SH) and diabetic ketoacidosis (DKA) events.
Results
Of the 77 adults enrolled, 63 completed the study. Those enrolled had mean (SD) baseline HbA1c of 9.8% (1.9%), 36% had T1D, and 44% were ≥65 years old. Mean HbA1c decreased by 1.3, 1.0 and 1.0 percentage points for participants with T1D, T2D or age ≥65, respectively (p < .001 for each). CGM‐based metrics including time in range also improved significantly. SH events decreased from the run‐in period (67.3 per 100 person‐years) to the intervention period (17.0 per 100 person‐years). Three DKA events unrelated to CGM use occurred during the total intervention period.
Conclusions
Non‐adjunctive use of the Dexcom G6 CGM system improved glycaemic control and was safe for adults using IIT.
ANSHIN is a single‐arm, prospective, interventional study that assessed the impact of non‐adjunctive Dexcom G6 continuous glucose monitoring (CGM) in adults with diabetes using intensive insulin therapy. After initiating CGM use, severe hypoglycemic events decreased and mean HbA1c decreased by 1.3, 1.0, and 1.0 percentage points for participants with type 1 diabetes (T1D), type 2 diabetes (T2D), or age ≥65, respectively (p<.001 for each) after 16 weeks. Non‐adjunctive use of CGM improved glycemic control and may help those with T1D or T2D make safe treatment decisions.
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